diff --git a/26_croston.ipynb b/26_croston.ipynb new file mode 100644 index 0000000..882ddad --- /dev/null +++ b/26_croston.ipynb @@ -0,0 +1,280 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "81a34b98", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e1cf585c", + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams[\"figure.figsize\"] = (9,6)" + ] + }, + { + "cell_type": "markdown", + "id": "97dfdc88", + "metadata": {}, + "source": [ + "# Croston's method" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "67466e30", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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unique_iddsy
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" + ], + "text/plain": [ + " unique_id ds y\n", + "0 1 2023-01-01 00:00:00 43\n", + "1 1 2023-01-01 01:00:00 87\n", + "2 1 2023-01-01 02:00:00 89\n", + "3 1 2023-01-01 03:00:00 87\n", + "4 1 2023-01-01 04:00:00 73" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('data/intermittent_time_series.csv')\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e9bc1753", + "metadata": {}, + "outputs": [], + "source": [ + "from statsforecast import StatsForecast\n", + "from statsforecast.models import CrostonClassic\n", + "\n", + "models = [CrostonClassic()]\n", + "\n", + "sf = StatsForecast(\n", + " df=df,\n", + " models=models,\n", + " freq='H',\n", + " n_jobs=-1\n", + ")\n", + "\n", + "cv_df = sf.cross_validation(\n", + " df=df,\n", + " h=1,\n", + " step_size=1,\n", + " n_windows=50\n", + ")\n", + "\n", + "cv_df.index = np.arange(50, 100, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0ddc8e14", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Z9Q7ekyZN0jPPPKOPP/5YgYGB+tvf/qZ169ZpzJgxat26tRU1AgAAAADgs+odvH/++Wede+65kiSn06ni4mIZhqHbb79dL730UqMXCAAAAACAL6t38I6JiVFhYaEkqVWrVsrKypIk5eXlqaSkpHGrAwAAAADAx9U5eK9cuVKSNHDgQM2dO1eSNGbMGN1222264YYbdPnll2vYsGGWFAkAAAAAgK+q86zmvXr1Us+ePTVq1ChdfvnlkqR7771XAQEBWrhwoUaPHq0HHnjAskIBAAAAAPBFde7x/vbbb9WrVy899dRTat++vX7/+99r/vz5uvvuu/Xhhx9q6tSpio6OtrJWAAAAAAB8Tp2Dd79+/TRz5kxlZ2drxowZ2rlzp4YPH6727dvrscce086dO62sEwAAAAAAn1TvydWCg4N19dVXa968edqwYYMuv/xyvfjii0pPT9c555xjRY0AAAAAAPisegfvw7Vv315//vOfdd999ykiIkJz5sxprLoAAAAAAGgW6jy52m/Nnz9f//rXv/Tee+/J4XBozJgxuu666xqzNgAAAAAAfF69gveOHTv0yiuv6JVXXtGWLVvUv39/PffccxozZoxCQ0OtqhEAAAAAAJ9V5+A9YsQIff3114qPj9dVV12lsWPH6qSTTrKyNgAAAAAAfF6dg3dwcLDee+89nXfeeXI4HFbWBAAAAABAs1Hn4P3hhx9aWQcAAAAAAM1Sg2Y1BwAAAADAbrvySrXjYIndZdSK4A0AAAAA8GmtooK1t6BMa3bn213KUZ3wcmIAAAAAAHiLPm1j7C6hVvR4AwAAAAB8kmmayi2usLuM46LHGwCOIysrq17tMzMzLaoEAAAAh5u7dq9uf2elxg3toPFDO9hdTq3o8QYAAAAA+By329TUuRtUXOFScXmV3eUcE8EbAAAAAOBzPsnao5+yCxXu9Nf/DWpndznHRPAGAAAAADSKSpdbn6/JVn5JpaXnqXK5NXXuBknS9QPbKSok0NLzNRTBGwAAAADQYKZp6i/vr9YjH6/V5v1Flp7rvyt3a/O+YkWFBGjs6W0tPVdjIHgDAAAAABrs5W+36t1lOxXm9FdsqNOy81S63PrblxslSTcNbq/woADLztVYCN4AAAAAgAZZuHG/HvtknSTp4l6pah0bIql6SHhjW7UjT3vySxUXFqir+rVp9ONbgeXEAAAAAHgllvT0DdsOFGv8W8vlcpsa3auVrh+YLtM09cYP2/XKt1s0e/wARTRir3SftjH66s4h2nqgWCGBvhFp6fEGAAAAAJyQovIq3fDaUuWXVurktChNuqibDMNQaaVLMxds1s/7ijXxv2sa/bxpMSEamBHf6Me1CsEbAAAAAHBC7pu9Whv2Fikh3KkX/9BbQQEOSVJIoL+eubSH/Azp/RW79L8f9zT4XKUVLq3Znd/g49iB4A0AAAAAOCF/HNJeHRLC9NJVfZQYEVRjX+82MRo3pIMk6S+zVys7v6xB53rtu60699mF+uvHaxt0HDsQvAEAAAAAJ6RTUoTmTBikk9Oijrr/tuEZ6tYqUvmllbrrP6vkdpsndJ6SCrdemP+zJKlzcsSJlmsbgjcAAAAAoM7W7M7Xkq0HPc8dfkatbQMcfnrm0h5y+vvpm4379fr3207onP/9qUC5JZVqFx+qUSennNAx7ETwBgAAAADUSX6ZS//32jJdMfN7ff1TTp1e0yEhXH85p7McfoaKyqvqfc7CcpdmryuQJN0+vKP8Hb4XY31j7nUAAAAAgK0qXaYmL9inXXnlSo8LVa/W0XV+7VX92ui0drE6KSm83uedva5AJZWmOiWF69xuyfV+vTfwvY8KAB9SVuk64ftYAAAAAG/y0tKDysopV5jTXzOv6q3IkLqvzW0YRo3QbZp1e4+cX+bShz8VSpLuGNFRfscY1u7NCN6ARSZ/uk6nPvaFfthy8PiNAQAAAC/2yYZCfbqxSIakZy8/WR0S6t9zfciGvYW6YPq3Wrfv+LOc7yioVJC/oYyYQI3oknjC57QbwRuwSEFppQrKqvTe8p12lwIAAACcsNV7y/TikurOpKtOjtIZnRoWgP/xzWat3pWvp789oNJK9zHbZiYE6R+jWunugXEyDN/s7ZYI3oBlLu6VKkn6dPUelVTUfxIJAAAAwBss2FoslykNbhui33Vt+FJe95/XRa2igpVdVKWZy3KP2z7I30/J4XUf1u6NCN6ARXq3iVab2BAVV7j0WVa23eUAAAAAJ2TcqTG6uW+MbjkttlF6nSOCAvT0mB4yJH2+qUg/7Cg5ok1OUZUWbC2Wu473gns7ZjUHLGIYhkb3TNUzX2zQe8t3avQvPeAA0BJlZWXVq31mZqZFlQAA6uLQ5GeGYcgwDJ2VceL3dB/Nae1idVGXCL2/tkDPfn9A0+Ocig52ePbPWp2vz38u0o97w3Rz39hGPbcd6PEGLDS6VytJ0qKfD2h3XqnN1QAAGsO+wnLtziv1PArLKj37KqrcNfb99pFfWnmMIwOA95i1Ol9PfLNfZVXHvge7If7QI0ptowKUX+7Wc98f8IT9XQWV+mJzkSRpeLswy87flOjxBiyUFhOivukx+mHLQc1esUvjh3awuyQAwAkqq3Tpjn+v1Cera94+9KeRHXXzGRmSpI05hTr32YW1HiPQ308f3jxAnZIafo8kAFhl2e5SvfljviTp9DYhOr1NqCXnCXAY+tOAOE34dI8KK9wqqTQVGmjo7R/z5TalU1oFq1O805JzNzWCN2Cxa/q3VZ+20Tq3W7LdpQAATlB+aaVueHWpFm+tntU30PHroMHD15Q1ZNTYd7jESKdm/V8/tYoKtrZYAGiAonKX/vbdAUnSuR3DLAvdh7SNDtQTI5PUISZQDj9D2/IqNH9rsSTpyu6Rlp67KRG8AYud3S1ZZxO6AcCnjXtzmRZvPahwp79mXt1Hp7U7+v2GXVIitOGxs5u4OgBoPDOX5epgqUutwv11ba/oJjnnSXG/9mq/+WO+TEn9W4eoQ2zz6O2WuMcbAADguO4+s5PS40L1zo39ag3d9eFyN49ZegE0L4t3lujLzcUyJE3oH6sg/6aNiwdLqrRoe/UM582pt1sieANNwuU29cXavbr9nZUqr3LZXQ4AoA7KKn/9e90jLUpzbx+kLikNuzf7m437dNHz3+r+D+o3yzsAWK2o3KXpP1TfTnNR5wh1jg9q8ho2HqyQnyGd2SFMbaICm/z8VmKoOdBE7v8gS9kFZRrRJVHnMPQcgA9qSUuCLdy4X3f8e6VmXtVHPdKiJEn+tdy7XR+GDK3YnqftB0r06KhMOfwavh4uADSGPUVVMk0pNcJfV/awp7e5b2qIXrs4VeGBza9/uPl9R4AXcvgZuuiXpcXeW7bT5moAAMfy8Y+7de0ri5VTWK6Z32xu1GP3bRejyOAAHSiu0LJtuY16bABoiIxYp54/P1n3DU6Qs4mHmB8uKsjRLD+UJHgDTeTiXqmSpHkb9mlfYbnN1QAAjubVRVt1y9srVOkydW73ZD09pkejHj/A4adhnRIkSZ9lZR+nNQA0rXCnQ2mRAXaX0SwRvIEm0iEhTD3SouRym/rvyl12lwMAOIxpmnpjVZ4e+nCNTFO6ql8bPXtZTzn9HY1+rjMzkyRJc9ZkyzSZZA2AvR78b5a+2lzE3yOLEbyBJvS7X4abv7+c4A0A3sLlNvX3Hw5q1up8SdLtwzvq4Qu6WjbUcVBGvIIC/LQrr1RrdhdYcg4AqIs5a7L12nfbNO27A9pZUGV3Oc0awRtoQuf3SFGgw09r9xRo3R7ebAGAtzhQ6pIh6dFRmbpteIYMw7r7C4MDHRrSkeHmAOyVW1yh+2ZXT5o5uksEQ8wtxqzmQBOKCgnUsM4J2nqgRPmllXaXAwBQ9QSY9wyM07p95fr9aW2a5JwXnJwiSerdJrpJzgcAv/XQh2u0v6hcGQlhuqJ7lN3lNHsEb6CJTR1zsoIDG/+eQQBA3eWWujT35yJd0jVChmEoyN9PPZODm+z853RLZmlJALZZtL1EH67aJ4efoacu6SFHPqvuWI3gDTQxQjcA2GtPYaUe+DJH2UVVMiRdkmnPerUAYIf8MpeeX3xAknTjoHbqkRalLIK35bjHG7BJUXmVFv283+4yAKBFqXCZenT+PmUXVSkpzF+ntwmxtZ7N+4r07tIdttYAoGVZurtUeWVudUwM023DM+wup8Wgxxuwwe68Ug17er5cpqnXLkpRmJNecABoCq+vzNO2vEpFBflpyshExYTY91boQFG5hk2dL9OUTs+IU3Jk0w11B9ByDWsXpoRQf3XrlGHJkok4Onq8ARskRwapTWyIKqrc+mZbid3lAECLsHpvmT5YV72ixK2nxdoauiUpNsyp3q2rJ1f7fM1eW2sB0LJ0SwxSZitus2lKBG/ABoZh6OJeqZKkLzcX21wNADR/JRVuPbNov0xJIzuE6dRUe4eYH3Jm1yRJ1WvpAoCVZq3O055CVtWxC8EbsMmFJ6fIz5B+2l+uXQX8EQQAK63ZV66DpS4lhvnr+t7es4TXoeD9w5aDyi2usLkaAM3VN9uK9caqfE34ZI+KKtx2l9MicY83YJOEiCAN6hiveev36avNRfrDyd7zRhCA9bKysurVPjMz06JKWoZTWgVr6tnJqnSZCgnwnn6H1rEh6pwcoXV7CvTFur26pE+a3SUBaGbyylyasfigJOmCThEKC/Sev4EtCf/qgI0ODTf/akux3KZpczUA0Ly1iw7USXFOu8s4wlkMNwdgEdM09fzigyoodys9OkBjWD7RNgRvwEYjuiQqNMDQ/mKXNh9kiCEANCbTNDVz6UFtOlBudynHdGZmoiRp2bZcVVQxBBRA41mwrUSLtpfIYUi394tTgMOwu6QWi6HmgI2CAhy66/R4tY4KUEIov44N8cGKXXruq4217r/+5DB1SwySJC3aXqLXVuZKkiKDHOqbGqIBrUOUGMbPAGhO/r10h/77U6HmbCrSyxe1UriXLt14UmK4Xr72FPVrF6tAf/pEADSO3FKXXlxSPcT80m6RahcTaHNFLRvvMgGb9WnVuOu2utymDhSXy2EYig37dUjlweIKVbmP3pPiZxiKO6xtbnGFKmtpa6jmJ6WF5S4d6qAJd/rJ38+eT1LzSyv1877aZ4gvqwr1fF1c6dbOgipJ0s6CKq3JKde/lucqIyZQA9qEaFi7MEtr/Sm7QBVVbnVPjbL0PEBLtv1AiR75aK0k6fJukV4buqXqlS6GnpRgdxkAmpnZawtUUO5WO4aYewWCN+BFXG5TjgYE16/X5+j+2VnalVeqtJhgfXP3GZ59V/3rB2XtKjjq6+LCArX0/hGe5ze+vkyLtx48atvQQIfeGZPqef7Ut/u1bHeZJCkxzF+Thic2Sc/xjoMleu6rjbrvnC6KDAnQmV2TdFJSeK3t3bk7PV/3SQnW5BGJMk1pW36FFm0v0Zqccm08WKGNByvUJ+XXD0PKKl0NqnNvQZmWbs1Vv/axigmt/qT5y3U5enLOeo3pk6qHL8hUcKD3BgLAF7ncpu58d6WKK1zqmuDUqM4RdpdUL6ZpyjAYDgqgYa7qGSVJGtEhzLaOEfyK4A14gW15FfrX8ly5TemvwxLr/frc4gr99eO1en/FLs82v9+8afMzDNX2N/e3b/AMQ7W2/e1xDVUf121Ke4uq9PiCfZpyZpKl9xC53KZuf2ellm7LVXGFS3+/opeSIoOUFBlU62uysvZ4vo4Odig6uDrsdk8K0vknRSivzKXvd5Top33lahMV4Gl7939+1Mqt+9S/dfVw9LZRAbW+IXa5TW3Nq9Ty77Zq6dZcLduWq115pZKk6Vf01HndUyRJma2qP3X+99KdWrkjT3+/opcyEmv/0ABA/cz8ZrOWbM1VaKBDt/ePbdAHmk3pH99s1jtLdugv53TW0E70gANoGH8/Q2O9aPnElo7gDXiBYH8/T69xTlGVEurYY2yapj5Zna2HPszS/qIK+RnS2AHpunPkSUf0on548+l1ruedG/sdc//hyyBNPKP6zWFOcZVu+98ebTxYoX8sy9UfT42p8/nq64X5P2vptlyFOf3157M6Ncoxo4IcOisjXGdl/BqAXW5TCzft18HiSm1fna9Zq/OVHO6vAb+E8A4xgZ4Qviq7VI/O26fSKlPSryHfz5A6JUXU+MBicMd4vXVDX902a6U27C3SBdO/1aOjMnVx719HEgA4MVtyKzT18x2SpIcu6KqkoHybK6q7rQeKtTGnSHPWZBO8AZyQtTll+nZ7ia7tFU0vt5dhBg/ACySE+at7YvU91l9tKarz65Zvz9X4t5Zrf1GFMhLC9N4f++v+87rYMnQ5IdRfdw6IkyT9b0OhFmyt/X7rhsjala9n5m6QJD10fhelxYRYch5JcvgZ+vpPQ3Rn/1idlhqsAD9pT2GV/rOmQLd/mq13sn59Q58cHqDSKlMhAYYGdYzX7cM76s3r++rHiWfqk9sG6pxuyTWO3b99nD65daAGdIhVaaVLd767Sne9u0qlFQ0b2g60dJ9tLFSFy60RXRJ1iY99mHVW1+q/E3PX7pXLzRKTAOpnf3GVJi3Yp//+VKj/rPGdDx1bCnq8AS8xrF2Yftxbrq82F+vSzMg63d/Xu02MLuiRoraxIRp/Rgc5/e29V7hPq2CNyYzQv7MKlF1U1ejHL61w6bZZK1TlNnV2ZpJ+1wRvqiODAzS0XZiGtgtTSaVbS3eVatH2Ei3ZVaqNB35dAi4+xKHp5yUrLSJAPbp3q9Ox48Odem1sX/39602a9sUGvb9il648rY1OTouy6LsBmr8bT4nRaZ3b6OxuyT53n3TfdjGKDA7QgeIKLd16UH3bxdpdEgAfUVbp0mPz9ymvzK22UQG6sJNvzW3REhC8AS/Rv3WIZiw5qN2FVfppf4U6xzuPaLO3qEoz3lquRy7o6pmx/G+XnexVby6v7B6lPinB6pJQ+/3WJ+rxT9fp533FSgh3atJF3Zr8+w4J8NOgtqEa1DZU5VVulbt+7ZEyDENto+q/TIfDz9CtwzLUp220Nu8rJnQDDeRnGPpDv7Z2l3FCAhx+GtY5Qe8v36U5a/YSvAHUiWma+sv7q7XxYIXCA/10/5B4BQcwsNnb8BMBvERwgJ/6t64eNv3l5prDzd2mqY9+KtD4j3frfz/u0WP/W+fZ502hW6oOkoeH7iq3KdNs+JDJ/NJKzVmzV5L05CU9FB1q71qUTn8/RTTi8kT928fp96e18Txfn12ov8xezdBzoA5KKtx6dUWuSiuPvgyiLzmra5Ikac6a7Eb52wmg+fvnwi16f8Uu+RnSPQPjlBQWcPwXockRvAEvMqxd9VrT32wrUcUvvak78it1z+d79eLSXJVVmTq1bYxuPqODnWXWWXZRpe76LFvvLNnR4GNFBgfoswkD9dQlPTS4Y3wjVOe9XG5Tt769Qm/9sF0X/n2hNu4ttLskwKu9tOyg3l1ToEkL9tldSoMN6hiv4ACHduWVas3uoy8BCQCHLNy4X5M+qe6Qua5XtE5ODj7OK2AXgjfgRbolBql/Woiu7RmlKrepf2fl65b/7da6feUK9jc07tQYzfq/09QuPszuUutk0fZSbTxYoQc/XKM1uxs+yUdUSGCT3NdtN4efoYkXdFV8uNMz6/l/lu08/guBFuj7HSX64udiGZIu7RZpdzkNFhTg0KieKbq4V6qc/rxNA3Bs5VUuBQc4dHGvVF3QiaVJvRn3eANexM8w9JfB1b25s1bn6Y1V1WG1d0qQxveNVUKov/x8aGmIUZ3DtXpvmZbsKtW4N5fro1vqvqTZIf9duUuVLlMX92rldcPqrdSvfaw+uXWgbn9npRZu2q8/vbtK328+oEcu7KqQQP50t3SHL+lXF5mZmRZVYq+8Mpee+/6AJOmiLhHKtGBuCTtMHt3d7hIA+IhhnRP14S2nq1VUsDatX3f8F8A2fJQKeKnzT4pQenSA7ugfq4lDE5QQ6nthy88wdEf/WLWKCta2AyW6+90f63XPYnZRpe6bnaU/vbtKn2VlW1ipd4oPd+rVsafqzhEd5WdI/1m2UxdO/1Z7C8rsLg2wnWmaeu77A8ovr57B9w89ouwuCQCahGmayi39dQ6Y9vFhCgqwd2UbHJ9PBe/JkyfLMAxNmDDBs800TU2cOFEpKSkKDg7WkCFDtGbNGvuKBBpJaKCf/nZOss5oF+bTPb3hTof+fmUvBTgMfbYmWx/+VLf7lV1uU1O/PaCi8iqd0jZaI3+ZcKilcfgZumVYht68/jTFhzsVGxaouLBfZ7wvq2TyNbRMz3x3QD/sLJW/n3THgDgFOHz37+TRmKapH3fm6fvNB+wuBYCX+XdWgW7+eLey9vJBvC/xmeC9ZMkSvfTSS+revebwqylTpmjq1KmaPn26lixZoqSkJI0YMUKFhUxGBN/n58OB+3Anp0Xp/nO7SJL+tTxX6/aVH/c1760t0Np95Qpz+mvqmJPl8KEh9lbo1z5Wn942UM9e3tPzb7HjYIl6PjJXt7y9Ql+s3auKKt+f0Rk4XE5BmT5fk61XV+TqiW9qTpyWX1b9odPve0SpXbS9qxxY4b3lu3TB9G81+ROGjgL41eKdJXpjVZ7yy93aWVBpdzmoB58Yu1pUVKQrr7xSM2fO1KOPPurZbpqmpk2bpvvuu0+jR4+WJL366qtKTEzUW2+9pRtvvNGukgH8xlX92mjx1oNav/OAooOP/ZnfpgPlenNVniRp4gVdlRYT0gQVer/De7ol6ct1e1Va6dJHq3bro1W7FRkcoHO6JevCk1N0atsYn5oPAJCqf/dXZZdp/YEKbf3oS+3Or9mbc0PvKsWEVL91GdU5QuedFKE+Kc3jvu7fGtwxXoYhrdqZr915pUqJYqZioKXbkV+pJ7/dL1PSOR3DdFYGk6n5Ep/o8R4/frzOPfdcDR8+vMb2LVu2KDs7WyNHjvRsczqdGjx4sBYtWtTUZQI4BsMwNOXi7nryzKRjri9ZVuXWU9/ul8uU+rcO0cW9WjVhlb7l6v5t9d/xAzR2QLriw53KL63U24u367KXvteAJ75qlJnkASuUVrq1bl+5PtlQqJLD1t7+YnOxXl6Rp0XbS7Q7v0yGIZ2UGK6R7cN0c98YBR42y3fP5GCd0irYp2/FOZb4cKd6t46WJH2+puXNcQGgpqIKtx6dl6PSSlNdE5y6oXeM3SWhnry+x3vWrFlavny5lixZcsS+7Ozq/4gSExNrbE9MTNS2bdtqPWZ5ebnKy38d6lpQwDqZQFMIdfrXuA9zb1GVEsNq/hlauadMuwqqFBPs0M19Y5rtm+rGYBiGeqRFqUdalO47t7O+33xA/125S59mZSuvpFLpcaGetku3HlR8uFNtYkOPcUSg8R0srtCqHXn6OitfW3IrtPlghXYXVunQNIttogLU9ZfZyE9OCtLBEpc6xgXqzD6d1C01UmFO/3rP4t5cnJWZpKXbcjVnzV5dMyDd7nIA2MTlNvXUwv3aVVil+BCH7h0U3+zmtWgJvDp479ixQ7fddps+//xzBQXVPpTst2/MTdM85pv1yZMn6+GHH260OgHUj2maemNVvv6zJl8Pn5Ggk5N/HUJ5WlqIHhueKFOmIpzM0FlXDj9DAzrEaUCHOD1yYabWZxfWWHbsvtlZWr+3UOlxoUf8Z901JVLPXHqy5/mlL36n3JKKI86REhWs5y7vqfCg2kcsoOVyuU3tLKjUltxKZSY4FffLSgwfrdqthz48ctLTmGCH0qMDasxlcVpaiE5Lq761JLN9bNMU7sXO7JqkR/+3Tj9sOaCDxRWKCW1+97IDOL7/bSjU0t2lCnQYum9wvKKCeH/ki7w6eC9btkw5OTnq3bu3Z5vL5dKCBQs0ffp0rV+/XlJ1z3dycrKnTU5OzhG94Ie79957dccdd3ieFxQUKC0tzYLvAMDRGIahg6VVcpnSU9/u19/OSVZsyK9/jronNc97NptKUIBDPdKiPM+Ly6uUEOHUxpxCbdlffET7iN8E6c37i7Wv8MgJ8DbsLdKbP2zXTYPbN3rN8F0/7CjRrKx8bcurVIWruh/79n6xGtY+TJKU2SpCGQlhSglxKz06UO2iA5UeHajoYN44Hk9aTIi6JEdo7Z4CfbFur8b04b0K0BKN7BCm9fvLdUqrEHWIdR7/BfBKXh28hw0bptWrV9fYdu2116pTp06655571K5dOyUlJWnu3Lnq2bOnJKmiokLz58/XE088UetxnU6nnE4uWsBON50So40HKrQ1r1JTFu5XZkKQhrUPVUo4vamNLdTpr9ev66t9heXamHPkig+/Dd4zruylClfNGdKXbMnVh6t2qRUTPOEw6/aVafI3+3RoQv0gf0Pp0YFy+v/ai927TYzm3jG4xQ4Xb6gzuyZp7Z4CLdiwj+ANtFBB/n7604A4br/zcV4dvMPDw5WZmVljW2hoqGJjYz3bJ0yYoEmTJikjI0MZGRmaNGmSQkJCdMUVV9hRMoA6cvr76d5B8Zrw6R6tySnXmpxy/W9Dof41qpVCAn1i3kefEx/uVHz48T907NP2yAlbTm0bo1vO6MBM6fDYV1ylx+ZXh+7T0oJ1bc9oJYf7N5tlEL3F7/qk6pT0aJ16lN9LAM3b/pIqxQY7ZBgGobsZ8OrgXRd33323SktLNW7cOOXm5qpv3776/PPPFR7O9PqAt2sVEaAJ/WI1ecF+SdLFXSII3V7K38HPBTV9vaVYeWVutY0K0J394xQcwDVihVZRwYw0AVqgsiq37vwsW0lh/rprQJxn3gz4Lp/7Cc6bN6/Gc8MwNHHiRE2cONGWegA0zIDWoRp/qlv7S6o0ukuE3eXgOMoqXXpv+U51SgpX7zb0wLVkl3SNUFign3qlBBG6AaCRfby+UAdKXHIYUiSTqTULPhe8ATQ/Z3dkhIqvmDp3g15asFmDO8br1bGn2l0ObHBo5RDDMHQOv7tNori8Sk/OWa9FP+/XR7ecLqc/b8KB5qyg3KV3s/IlSX/oEcXSYc0EH1EDAOrsyr6t5WdI8zfs04a9R07UhuZtyc4SPTJvn4or3MdvjEYTHODQp1l7tGFvka6c+YOe+OwnfZaVrT35pTJN8/gHAOBT3s0qUHGlqfToAA1OD7W7HDQSgjcAoM7axIbqzK5JkqR/fLPZ5mrQlDblFOrJb/drya5SfbCuwO5yWhQ/P8Mzo/nSbbmaMe9n3fTGMvWb/JX6TvpSq3fme9oSxAHfllNUpY/WV/+NvaZnNBNWNiMMNQcA1MsNg9rp06xsfbBit/505klKCGfd9eYuv6RSN7y2TCWVpromODUmM9LuklqcO0Z01NmZyVq1M08/7szTyh352rC3UDmF5WoV/evka099vl6frM5Wj9RIdU+NUo+0KHVNiVBQAMPTAV/w5o95qnJL3ROd6pXM/6/NCcEbAFAvvVpHq3ebaC3blqvXFm3Tn848ye6SYCGX29Qts1Zoy/5ixYc6dO+geO43tIFhGOqSEqEuKRG6/NTWkqTSCpfW7y1UTGigp92qHfnasr9YW/YX64OVuyVJ/n6G0uNCFREcoPf+2N/TduKHa7R028Faz/nxLQMt+m4AHE1ZpUubDlZIqu7tZgmx5oXgDQCotxsGpmvZtly9/v02jRvaXiGB/HfSXD3+6Tot2LBPQQF+emBwgqKYXddrBAc6dHJaVI1tz13eUz/uyteqHXnVj5152l9UoY05RQr6zezz2w4UK2vX8W8bME1TP2UXqnMyK08AVgoKcOjZc5K1em+ZOsY57S4HjYx3SgCAehvRJUnpcaFKjwtVXkklwbuZ+mpzkWYuOiBJeuqSHmrrV3vvKLxDdGigBneM1+CO8ZKqQ/Pu/DJtyik6ou3tIzrqqv5tj3m8iiq3LnvpOy3fnqc5EwbppCRmsges5PAzdHJy8PEbwufwTgkAUG8OP0Mf33K6Qp38N9KctY4MUEpkkC7unarzuqcoK4vg7WsMw1CrqGC1ijryjXz31Kjjvj7Q30+JEdX3mb4w/2c9c+nJjVwhANM09d7yXTqve7LdpcBCzGoOADghhO7mr0OsU/+7daBuH97R7lJgo3FDOkiSPly1WzsOlthcDdD8zFmTrT+9u0rnP7dQLjcrEzRXBG8AQIPszivVvxZusbsMNJIKl6nNv0zuI1UPXfbzY4KflqxbaqQGZsTJ5Tb14oKf7S4HaFaq3KamfLZeknR2ZpIc/L1ttgjeAIATVlRepeFT5+uRj9dq2bZcu8tBA5mmqek/HNCdn+3R/K3FdpcDLzJ+aHWv97+X7lROYZnN1QDNx9xNRdq8v1ixoYG6YVA7u8uBhQjeAIATFub017ndqu9J+8c3m22uBg31wbpCfbW5WC5TinTyFgG/6pseo16to1RR5dY/GeECNIqyKrfe+jFfknTLGR0UHhRgc0WwEv+rAgAa5PqB1Z/Qz1mTrW0H6CX1Vct2l+rlFdWjFq7vHc2suqjBMAxPr/d3Px+QaXIfKtBQ/11XqNwyl9JignVF3zZ2lwOLEbwBAA1yUlK4BneMl9sU93r7oNxSl5btLtWUhfvlNqUR7UN1/kksGYUjndEpQS9fc4pmjxsgw+A+VKAh8stc+s/a6t7uP408SYH+xLLmjp8wAKDBbvil1/vfS3cqr6TiOK3RlEzT1N6CMn2/+YDeWbJdr67Ilfuw3sqXlh7UQ1/lqLjCrU5xTo07NZZQhaMyDENDOyUw+RPQCMpdpronBql9TKDO755idzloAqwFAwBosAEdYtU5OULr9hTozR+2e4akwh7zthRr0Y4S7S6o1J7CKpW7ttfYf07HcMWHVr8FSI0IUHKYvzJiA3VDnxgFOAhVOL6ySpe2HihWp6QIu0sBfFJCqL8eGJKg0ko3K0e0EARvAECDGYahGwam64EPsrj3swm43Ka25VXqp/3lWv/L48kzkxTudEiStuZVaNH2X9db9jOk1OgQtY0LVYRRpsPf4l3ZI0pX9ohq2m8APm3dngJd9a/FchiGFtw9lCGyQAMEB/D701IQvAEAjeK87ika1jlRkcHMymqFrF35+ujH3VqxPU+rtueq3FXzA44NByrUO6V6QrS+qSGKDnYoJTxAKeH+GnpqD084ysrKavLa0by0iw+VISm7oEyzV+zUpae0trskwGdszq3Q/9YX6orukYoNIYq1JPy0AQCNItDfj56vRlBe5dbPByv00/4K9U0NVquI6g8ysnbl68X5vy7ZFhJg6KQ4Z/UjNlCd4pyefZ3jneoc/+tzfi5oTE5/h24Y2E6PfbJOL8zfrN/1TuO+b6COXl2Rq2W7y1ThMnXngDi7y0ETIngDABqVaZpasjVXQQF+6p4aZXc5PqPCZeqlpQc1d1ORDnVmBzoMT/A+NT1Gl5/aWj1bRymsbJ9SIwPkxyRosMkVfVtr+tebtGV/sT7N2qPzmBwKOK5V2aVatrtM/n7Sld0j7S4HTYyPwAEAjWrmN5s15sXv9OSc9XaX4jNyiqt095xsfbaxOnRHBfnptLRgJYQ6PG3axYdp8uhuGtMnTa2jAgndsFWo01/X9G8rSXr+65+Z2wE4DtM09cryPEnS2RnhSgrntqyWhuANAGhUZ2cmy8+Qvtm4X2t3F9hdjtdblV2qCZ/s0aaDFYpw+umRMxL0+sWpun9wgk5NDbG7PKBW1/Rvq5BAh9buKdC8DfvsLgfwagu3l2jjwQoF+xu6tBu93S0RwRsA0KjSYkJ0drdkSdI/Fm4+Tmt8t6NUBeVutY8J1LSzk9UrJZh1tOETokMDdcWp1ROrLdly0OZqAO9V5Tb12oo8SdLoLhGKCnIc+wVolrjHGwDQ6G4Y2E7/+3GPPlq1W/ec1UmJEUF2l+S1rusVrZhghy7sFC4nk6DBx/zf4Ha6qFcrdU2hBw+ozfwtxdpTVKWoID+N6hxhdzmwCf/DAwAa3clpUTq1bYwqXaZeWbTV7nK8ypb9xbpv9mq53NX3xAY4DI3JjCR0wyclhAcRuoHjGJIeqv5pIbqhdwzrdrdg/OQBAJa4fmC6JOnN77epuLzK5mq8wxdr9+qC5xbqzR+2a9bqfLvLARrVnvxS7ThYYncZgFeodJmeD1gdfobuHRSnwemhNlcFOxG8AQCWGN45UelxoYoODdT2Fv5m3G2aenNVnq5/bakKy6vUp020zs4Is7ssoNHMWrxdg6Z8rcc/+8nuUgDblVW59fDXOfr7Dwc9M/4zdwe4xxsAYAk/P0OvX3eqkiOD5fAztHV/sd5bvrPW9kNOilfvNjGSpF15pZq1eHutbQd0iNNp7WIlSTkFZXp9Zd5R26WE+2tIeqgcfva94SmqcOuphfu1dHepJOnqfm1037ldtOGntbbVBDS2HmlRqnSZ+mT1Hm3eV6R28XywhJYpv7RS93+Ro5/2lyvI39DoLhFKjWTpMBC8AQAWSo3+dTmsHbkleu6rTbW2jQwO8ATv7PzSY7Z1+vt5gvf+ogq9k1X7sO0dBZW6pmd0fUtvFNvyKvTovH3aU1SlQIehSaO763e9U22pBbBS5+QIDeuUoC9/ytGL8zfrid91t7skoMntLyrXH/65WD/tL1dooJ8eHppA6IYHwRsA0CSSI4N1db82te7vkvLrTK/xYUHHbNstNcrzdUxooM47KfyINmWVbq3KLtPpre1bC9uUlFvmUkKoQ38ZFK9RhG40Y+OGtteXP+Xo/RU7NWFEhpIjg+0uCWgyu/JK9Yd//KDN+4sVFeSnvw5LVHp0oN1lwYsQvAEATaJDQpgevjCzTm1bx4bUuW1SZJBuOiXmqPvKqtwKauLZwk3T9NzL1zYqUA8OSVDb6ABFOFm3Fc1b7zYx6pseox+2HNTMBVv04Pld7C4JaBK7Cir1fzMWaXd+mVpFBevBQdFqFUFPN2picjUAQLN1eOjedKBceWUuS89X4TL12Px9ysop82zrnhRE6EaLMW5oB0nS24u362Bxhc3VAE1jT2GVcgrL1S4uVO/e1I/QjaMieAMAmr3vd5To7s/3avKCfap0mZaco9Jl6rH5Ofp+Z6mmLTrgWUYGaEkGZcQps1WEDEP6cWee3eUATaJPq2C9dFVv/fumfkqJ4hYLHB1DzQEAzV6riAD5+0lrcso1c9lBPd+jcY9f6TI1acE+LdtdJqfD0K2nxdo6kzpgF8Mw9PQlJysh3KnoUO5vRfP1Y3aZ4kMdSg6v7t0+o1OizRXB29HjDQBo9tIiA/SnAXEyJH2yoUhvH2Opsvo6FLqX7CqV02HowaHx6p4U1GjHB3zNSUnhhG40a9/vKNGDX+3V/V/s1cGSKrvLgY+gxxsA0CKcmhqi3/eI0uur8vTgf7OUkRCmPm2PPilbXVW6TD3+TXXoDnQYemBIvHokMcwQOOTzNdlKiAiS8yiTHKbHhSoooHr+g32F5dpfVF7rcdrEhigksPpt64GicuUU1t42LSZEYc7qtrnFFcouKKu1bavoYEUEVfdY5pdUand+aa1tU6KCFRlc3bagrFK7cmtvmxwZpKiQ6g8fisqrtONgSa1tEyOCFMMHFT7j6y1FembRAblNqV1MoMKZwwN1RPAGALQYYzIj9HNuhRZtL9FNbyzXx7ec3qDjvb+2QD/srA7dDw6J18nJhG7gkFvfXqEPV+2udf+ntw1U5+TqZQTfXrxdU+duqLXt++P6q1fraEnS7BW79Oj/1tXa9s3r+2pAhzhJ0v9W79H9H2TV2nbmVX00okv1EOEvf9qrO/69qta2z17eUxf0SJEkLdq0Xze9sbzWtk9c3E2XntJakrR060Fd8/KSWts+dH4XXTsgvdb98B6fbCjUjMUHZUoamh6qCf24rQh1R/AGALQYhmHo9n6xOljh0E/ZhXpl0Vadl3bixxvVOVwbD5TrnI7hhG7gN87OTNKybbmqcLmPut//sMASEuhQfLiz1mMF+P3aYx58vLaOw9oGHLtt4GE98UHHaXt4r73T/9htD/XkHzrHsdoGH9a2vMolpz89qN7o2+3Fen7xQUnSuR3DdeMp0fIzCN2oO4I3AKBFCQ7w00t/6KMPVu7SzUM7aO3aNfV6vcttys+oDvFOfz/dPyTBokoB33Z2t2Sd3S25Tm2vH9hO1w9sV6e2V/Ztoyv7tqlT24t7p+ri3ql1antOt2SdU8d6h3ZK0JL7htepbf/2ccdtu3lfka5/banKKlxadO+wOh0XTcdtmnpzVb4k6byTwnVjn2gZhG7UE5OrAQBanNaxIbp1WIb86jlE0OU2devbK/T4Zz/JNFkuDEDjSIwI0tb9xdqdX6acwtrvSYc98svccvhJwQGGft8jitCNE0KPNwCgRatwmZqx+IAGtw095nBxl9vUU9/u1zfbShTgMPS7XnXrRQOA4wl1+qtDQpg27C3S6p35GtaZlRG8SXSwQ8+ek6zdhVUKC6TfEieGKwcA0KK9tyZfc38u1hPf7Fd2UeVR27jcpp4+LHS/8PveykgMb+JKATRn3VpFSZJ+3JlvbyE4KsMw1CoiwO4y4MPo8QYAtGgXd43U4l2l2nigQo/O26enzkpS0GGTKLncpqYu2q8F20rk7yfNuLK3hnVOtLFiNHdZWbXPwn00mZmZFlWCptQ9NVLvLd+p1bsI3t5k4bZi9UoJVkgA/ZVoGK4gAECLFugwdN+geEUF+WlrXqWmLTrguX/b5TY17bsDmr+1RA5D+vPAeA3vQugG0Pi6p0ZKkn7cmcccEl7i54MVevyb/brhg10qrTz67PxAXRG8AQAtXlyov/4yKF7+ftLC7SV6d02BJGntvnLN21IshyHdMzBep6WF2FwpgOaqc3KE/P0M7S+q0J58JljzBv9ZUz364OTkIAXT440GYqg5AACSuiQE6aZTYjT9h4N6fWWe0qMCdEpqiG7rF6uQAD/1b03oBmCdoACHzspMUkigQy43Pd5221VQqW+3l0iSLukaaXM1aA4I3gAA/OKsjHD9fLBCC3+ZRE2ShrcPs7kqAC3F9Ct62V0CfvH+2gK5TemUVsFqGx1odzloBgjeAAAc5v/6xOiSzEglhPJfJAC0RHsLyvTl5iJJ0iVdI2yuBs0FNysAAHCYAIdB6AZgm0qXW2t25zPBmo3+uXCLqtxS1wSnuiSwpjoaB8EbAAAA8AIut6lef52rc59dqO0HS+wup8XKL6mUIel33NuNRsRH+gAAAIAXcPgZahcfplU78vTjzny1iQ21u6QW6YnfddeIVi4lhhGV0Hjo8QYAAAC8RPdW1b2sq3fl21xJy5YUHiDDMOwuA80IwRsAAADwEt1Sq4P3qh159hbSAi3atF9b9xfbXQaaKcZPAAAAAF6iR2qUJClrV77cblN+fvS6NoVKl6k/vbtK2QVlenXsqYq2uyA0O/R4AwAAAF6ifXyoggMcKq5waTO9r01m/tZi7c4vU2yYU6e0jbG7HDRDBG8AAADAS/g7/NQ1pXrt6NW78uwtpoVwm6b+s6b6nvrrTk9XUIDD5orQHDHUHAAAAPAil56SpqGdEtStFctZNYXvd5RqZ0GVwoP8dWXf1naXg2aK4A0AAAB4kUv6pNldQothmqbe/aW3+6p+bRQeFGBzRWiuGGoOAAAAoEValV2mjQcqFOgwdO2AdLvLQTNG8AYAAAC8zM7cEn2yeo/25JfaXUqzdrDUpdBAP43sEKa4MKfd5aAZY6g5AAAA4GXuevdHfbf5gKZc3F1jTmHouVXOaBem09JCVOU27S4FzRw93gAAAICX6Z5aPbHaj8xsbrmQAD9FOJnJHNYieAMAAABepntqlCTpx5359hbSTO0prNSy3aUyTXq60TQYao5aZWVl1at9ZmamRZUALRO/gwDQch3q8V63p0DlVS45/emRbUz/zsrX3J+LdWGncN3QJ8buctAC0OMNAAAAeJnU6GBFhwSo0mVqQ3aR3eU0K/uLq/T1lmJJ0sA2oTZXg5aC4A0AAAB4GcMw1O2X4eardubZWktzM3tdgarcUrdEpzrFM5M5mgbBGwAAAPBC3VtVDzdfzX3ejaag3KU5m6pHEPyua6TN1aAl4R5vAAAAwAud3yNFnZLD1bN1tN2lNBsf/VSosipT7aMD1Ss5yO5y0IIQvAEAAAAvdFJSuE5KCre7jGajtNKtj9YXSpIuyYyQYRg2V4SWhKHmAAAAAJo9fz9DXeKdSgn3V7+0ELvLQQtDjzcAAADgpdbsztf8DfvUOTlC8XYX44MqXaYqXaZCAv0U4DD0x1NjtD2/Ug4/ervRtOjxBgAAALzU3LV7NeWz9fpo5W67S/E5a3LKdOsne/TC0oOebfGh/uqdEmxjVWip6PEGAAAAvFT31OqZt3/clS91jbW5msa1bk+BggMcahvXuGtp55dW6onPftJbP+yVJBWWu5Rf5lJkkKNRzwPUB8EbAAAA8FLdWkVJkn7eV6SSymiFBDSfAauTPlmnbzbu18CMOF3Zt7WGdU5UgOPEvz/TNPXJ6j166MM12ldYLkka2T5M1/aKUriT0A17EbwBAAAALxUf7lRKZJB255fp54MV6pbYPJbAqqhyy9/PkGFI32zcr2827ldCuFOXnZKmy05trZSo+g0Hzy116bnvD2jxru2SpHZxobqhZ1iz+feC72s+H5kBAAAAzVC3X4abbzxQYXMlDWOapq55ebGmf7VRFS63Xr72VC24a6jGDWmvuLBA5RSW69mvNun0J77SE5/9VK9jBzgMbTxQoQCHoVuHZeiT2wYSuuFVCN4AAACAF+ueGiVJ2nSg3N5CGmjehn2at36fpn+9SaUVLklSWkyI7j6rkxb9eZimX9FT/drFym1W91gfUlLpVm6p64jj7SqolGmakqSwQD/96fRYfXLrQN0xoqOCAhhaDu/CUHMAAADAix2aYG1LXqXNlZw40zQ1be4GSdIfTmuj+HBnjf2B/n46r3uKzuueok05RUqN/nWo+dyfi/Ty8lz1SwvR2Rnh6hgXqFmr8/X+2gLd3i9WQ9uFSZJ6JAUrIzG86b4poB4I3gAAAIAX690mWh/fcroq92+3u5QT9vX6HK3ama/gAIduHNz+mG07JITVeL7pQIWq3NI320r0zbYSBToMVbiqe7rX7Cv3BG/AmxG8AQAAAC8WEuivzFaRyso17C7lhJimqWlfbJQkXdWvjeLCnMd5RU13DojTRZ0j9OnGQs3bUqzSKlOxIQ6NOyVGfdNCrCgZaHQEbwAAAACW+eqnHP24M18hgQ7936B2J3SMdjGBGt83Vtf2itbanHJ1SXA2q6XV0PxxtQIAAABebu3uAj37/QH9a3mu3aXU27NfbZIkXdWvrWLr2dv9WyEBfurTKpjQDZ/DFQsAAAB4ufzSSn2+qUjfbCu2u5R6mzqmhy7pnaobBqbbXQpgG4aaAwAAAF4us1WEDEn7il3KK3MpKsh3lstqHx+mJy/pYXcZgK3o8QYAAAC8XHhQgFpFVPeZbTxQYXM1dVNR5ba7BMBrELwBAAAAH5ARW31/9KYD5TZXcnymaeriGYt0+zsrlVNQZnc5gO0I3gAAAIAPyIgNlOQbPd5z1uzV6l35mrt2rwIcRA6A3wIAAADABxwevE3TtLma2rlNU9O+2CBJuqZ/W0WHBtpcEWA/gjcAAADgA9KjA+UwpKAAQyWV3hu8v99Rqp+yCxXm9Nf1zGQOSGJWcwAAAMAnBPn76e0xaV69hrXbNPXWj3mSpGsHtFVUCL3dgESPNwAAAOAzvDl0S9Ki7SXamlepcKe/rj+9nd3lAF7Du39zAQAAABzBW+/x/nh9oSTp2tPTFRkSYHM1gPdgqDkAAADgIw6UVOmpb/drX7FLMy9MkWEYdpdUw4NDEvTxhkJddzr3dgOHo8cbAAAA8BERTofW7StXdlGV9hZX2V3OEUIC/TQmM1KRwfR2A4cjeAMAAAA+IsBhqG1U9YRlm7xoPe/cUpfXDn8HvAHBGwAAAPAhHQ5bz9sbuNym/vLFXt3xWbZ25lfaXQ7glQjeAAAAgA/JiDkUvMsbdJyi8ip9sGKXth0obtBxFm4r0Y78Su0urFJ0sKNBxwKaKyZXAwAAAHxIRpxTkrTpYIXcpim/E5hgbcfBEl336hJt2FskSerdJlpPXNxNHRLC63Ucl9vU26vzJEmjOoUrNJB+PeBo+M0AAAAAfEjryAAFOgyVVJraU1j/CdaWbcvVqL9/qw17ixTu9JefIa3ela/48CBPm+0HSlRe5Trusb7ZVqKdBVUKC/TThZ0i6l0L0FLQ4w0AAAD4EH8/Qz2SguQ2TVW46jeh2X9X7tJd//lRFVVudU2J0D+u7iM/w9CPO/NrzER+y6wV2rKvSOd2T9ZFPVMVdJSedZfb1Kxfersv6hyhEHq7gVoRvAEAAAAf89DQhHq/xjRNfbEuRxVVbo3okqhpl56sUGd1HBjR5dfe7vzSSuUUlKmgrEpvL96htxfvUEKoQ0PSQzU0PUxpkdUBfcHWYu0sqFJ4oJ/OP6l+Q9SBlobgDQAAALQAhmHoyd91V+/WUfpDv7Zy+B393vDI4AB9e88Z+n7LAX2wYpc+WZ2tnOIq/TurQP/OKtCozuG6vneMFu0okSRd1IXebuB4+A0BAAAAfFRuqUsud+3DzfcXlWvaFxvk/qVNUIBD1wxIrzV0H+LnZ6h/+zhN+V0PLb1/uO4+PU6ntAqWw5A6/TK5272D4vXngXE6j95u4Ljo8QYAAAB8jGmaGvfxHu3Ir9T085LVNirwiDZb8yp04/RvtSuvVA7D0C3DMk7oXEEBDg1qG6pBbUOVX+ZSSEB1352fYej0NqEN+j6AloIebwAAAMDHGIahSGf1W/lNByqO2L90V6numpOtXXmlahsbonO7JzfKeSODHApw1H/5MqClI3gDAAAAPigjtnrI98bfBO+PfirQI/NyVFppqm96jGaPG6B28WF2lAjgFww1BwAAAHxQh9jq4eUbD5RLql7e66WlufrfhkJJ0vB2oXp+bF8F+tPXBtiN4A0AAAD4oIxfgveW3ApVukztKKjUnE3VofuanlG6uEsEoRvwEgRvAAAAwAclh/krNNBPxRVubc+vVPuYQE3oF6dAh6H+rUPsLg/AYfgIDAAAAPBBhmEoPSpAoQGGNv0y3HxIeiihG/BCBG8AAADAR52cHKSQQD/1Tgm2uxQAx8BQcwAAAMBHje4SqYxYp8Kc9KcB3ozgDQAAAPioQIdBbzfgA/hoDAAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALeXXwnjx5sk455RSFh4crISFBo0aN0vr162u0MU1TEydOVEpKioKDgzVkyBCtWbPGpooBAAAAAKjJq4P3/PnzNX78eH3//feaO3euqqqqNHLkSBUXF3vaTJkyRVOnTtX06dO1ZMkSJSUlacSIESosLLSxcgAAAAAAqvnbXcCxfPbZZzWev/zyy0pISNCyZcs0aNAgmaapadOm6b777tPo0aMlSa+++qoSExP11ltv6cYbb7SjbAAAAAAAPLy6x/u38vPzJUkxMTGSpC1btig7O1sjR470tHE6nRo8eLAWLVpU63HKy8tVUFBQ4wEAAAAAgBV8Jnibpqk77rhDp59+ujIzMyVJ2dnZkqTExMQabRMTEz37jmby5MmKjIz0PNLS0qwrHAAAAADQovlM8L755pv1448/6u233z5in2EYNZ6bpnnEtsPde++9ys/P9zx27NjR6PUCAAAAACB5+T3eh9xyyy368MMPtWDBAqWmpnq2JyUlSaru+U5OTvZsz8nJOaIX/HBOp1NOp9O6ggEAAAAA+IVX93ibpqmbb75Z77//vr766iulp6fX2J+enq6kpCTNnTvXs62iokLz589X//79m7pcAAAAAACO4NU93uPHj9dbb72l//73vwoPD/fctx0ZGang4GAZhqEJEyZo0qRJysjIUEZGhiZNmqSQkBBdccUVNlcPAAAAAICXB+8ZM2ZIkoYMGVJj+8svv6xrrrlGknT33XertLRU48aNU25urvr27avPP/9c4eHhTVwtAAAAAABH8urgbZrmcdsYhqGJEydq4sSJ1hcEAAAAAEA9eXXwBgBfl5WVVa/2h5ZLBAAAQPPh1ZOrAQAAAADg6wjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICF/O0uAAAAAPbLysqqV/vMzEyLKkFzw7UF0OMNAAAAAIClCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIjgDQAAAACAhQjeAAAAAABYiOANAAAAAICFCN4AAAAAAFjI3+4CAAAAAFgvKyurXu0zMzMtqgRoeejxBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALMRyYkAjYHkOAAAAALWhxxsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQy4kBAGpgeTwAsA5/Y9HccE3XDT3eAAAAAABYiOANAAAAAICFCN4AAAAAAFiI4A0AAAAAgIUI3gAAAAAAWIhZzX0AMwUCAAAAgO+ixxsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALCQv90FAAAANDdZWVn1ap+ZmWlRJQAAb0CPNwAAAAAAFiJ4AwAAAABgIYI3AAAAAAAWIngDAAAAAGAhgjcAAAAAABYieAMAAAAAYCGCNwAAAAAAFiJ4AwAAAABgoWYTvJ9//nmlp6crKChIvXv31jfffGN3SQAAAAAANI/g/c4772jChAm67777tGLFCg0cOFBnn322tm/fbndpAAAAAIAWrlkE76lTp+q6667T9ddfr86dO2vatGlKS0vTjBkz7C4NAAAAANDC+Xzwrqio0LJlyzRy5Mga20eOHKlFixbZVBUAAAAAANX87S6gofbv3y+Xy6XExMQa2xMTE5WdnX3U15SXl6u8vNzzPD8/X5JUXFx8RNuCggLP10VFRfWq7fDXNkRDzstr6/7ahvDFmhvCF79fX7wmffHfyi6++DPitd7/2oZoaTWj7pry2mjo63ktr/Xm1za2Q8c2TdOS4/t88D7EMIwaz03TPGLbIZMnT9bDDz98xPbhw4dbUhsAAAAAwPsdOHBAkZGRjX5cnw/ecXFxcjgcR/Ru5+TkHNELfsi9996rO+64w/M8Ly9Pbdq00fbt2y35R0bLVFBQoLS0NO3YsUMRERF2l4NmgusKVuC6ghW4rmAVri1YIT8/X61bt1ZMTIwlx/f54B0YGKjevXtr7ty5uuiiizzb586dqwsvvPCor3E6nXI6nUdsj4yM5JcXjS4iIoLrCo2O6wpW4LqCFbiuYBWuLVjBz8+aadB8PnhL0h133KE//OEP6tOnj/r166eXXnpJ27dv10033WR3aQAAAACAFq5ZBO9LL71UBw4c0COPPKI9e/YoMzNTn3zyidq0aWN3aQAAAACAFq5ZBG9JGjdunMaNG3dCr3U6nXrooYeOOvwcOFFcV7AC1xWswHUFK3BdwSpcW7CC1deVYVo1XzoAAAAAAJA1d44DAAAAAABJBG8AAAAAACxF8AYAAAAAwEItNnhPnjxZhmFowoQJnm2maWrixIlKSUlRcHCwhgwZojVr1thXJLzexIkTZRhGjUdSUpJnP9cUTtSuXbv0+9//XrGxsQoJCdHJJ5+sZcuWefZzbeFEtG3b9oi/WYZhaPz48ZK4rnBiqqqqdP/99ys9PV3BwcFq166dHnnkEbndbk8bri2ciMLCQk2YMEFt2rRRcHCw+vfvryVLlnj2c13heBYsWKDzzz9fKSkpMgxDH3zwQY39dbmGysvLdcsttyguLk6hoaG64IILtHPnznrX0iKD95IlS/TSSy+pe/fuNbZPmTJFU6dO1fTp07VkyRIlJSVpxIgRKiwstKlS+IKuXbtqz549nsfq1as9+7imcCJyc3M1YMAABQQE6NNPP9XatWv19NNPKyoqytOGawsnYsmSJTX+Xs2dO1eSdMkll0jiusKJeeKJJ/TCCy9o+vTpWrdunaZMmaInn3xSzz33nKcN1xZOxPXXX6+5c+fq9ddf1+rVqzVy5EgNHz5cu3btksR1heMrLi5Wjx49NH369KPur8s1NGHCBM2ePVuzZs3SwoULVVRUpPPOO08ul6t+xZgtTGFhoZmRkWHOnTvXHDx4sHnbbbeZpmmabrfbTEpKMh9//HFP27KyMjMyMtJ84YUXbKoW3u6hhx4ye/TocdR9XFM4Uffcc495+umn17qfawuN5bbbbjPbt29vut1uriucsHPPPdccO3ZsjW2jR482f//735umyd8snJiSkhLT4XCYH3/8cY3tPXr0MO+77z6uK9SbJHP27Nme53W5hvLy8syAgABz1qxZnja7du0y/fz8zM8++6xe529xPd7jx4/Xueeeq+HDh9fYvmXLFmVnZ2vkyJGebU6nU4MHD9aiRYuaukz4kI0bNyolJUXp6em67LLLtHnzZklcUzhxH374ofr06aNLLrlECQkJ6tmzp2bOnOnZz7WFxlBRUaE33nhDY8eOlWEYXFc4Yaeffrq+/PJLbdiwQZK0atUqLVy4UOecc44k/mbhxFRVVcnlcikoKKjG9uDgYC1cuJDrCg1Wl2to2bJlqqysrNEmJSVFmZmZ9b7OWlTwnjVrlpYvX67JkycfsS87O1uSlJiYWGN7YmKiZx/wW3379tVrr72mOXPmaObMmcrOzlb//v114MABrimcsM2bN2vGjBnKyMjQnDlzdNNNN+nWW2/Va6+9Jom/V2gcH3zwgfLy8nTNNddI4rrCibvnnnt0+eWXq1OnTgoICFDPnj01YcIEXX755ZK4tnBiwsPD1a9fP/31r3/V7t275XK59MYbb+iHH37Qnj17uK7QYHW5hrKzsxUYGKjo6Oha29SVfwNq9Sk7duzQbbfdps8///yIT84OZxhGjeemaR6xDTjk7LPP9nzdrVs39evXT+3bt9err76q0047TRLXFOrP7XarT58+mjRpkiSpZ8+eWrNmjWbMmKGrrrrK045rCw3xz3/+U2effbZSUlJqbOe6Qn298847euONN/TWW2+pa9euWrlypSZMmKCUlBRdffXVnnZcW6iv119/XWPHjlWrVq3kcDjUq1cvXXHFFVq+fLmnDdcVGupErqETuc5aTI/3smXLlJOTo969e8vf31/+/v6aP3++nn32Wfn7+3s+6fjtJxc5OTlHfAoC1CY0NFTdunXTxo0bPbObc02hvpKTk9WlS5ca2zp37qzt27dLEtcWGmzbtm364osvdP3113u2cV3hRN11113685//rMsuu0zdunXTH/7wB91+++2eEYZcWzhR7du31/z581VUVKQdO3Zo8eLFqqysVHp6OtcVGqwu11BSUpIqKiqUm5tba5u6ajHBe9iwYVq9erVWrlzpefTp00dXXnmlVq5cqXbt2ikpKckzw6tUff/b/Pnz1b9/fxsrhy8pLy/XunXrlJyc7PlPgWsK9TVgwACtX7++xrYNGzaoTZs2ksS1hQZ7+eWXlZCQoHPPPdezjesKJ6qkpER+fjXfUjocDs9yYlxbaKjQ0FAlJycrNzdXc+bM0YUXXsh1hQaryzXUu3dvBQQE1GizZ88eZWVl1f86O7E54ZqHw2c1N03TfPzxx83IyEjz/fffN1evXm1efvnlZnJysllQUGBfkfBqd955pzlv3jxz8+bN5vfff2+ed955Znh4uLl161bTNLmmcGIWL15s+vv7m4899pi5ceNG88033zRDQkLMN954w9OGawsnyuVyma1btzbvueeeI/ZxXeFEXH311WarVq3Mjz/+2NyyZYv5/vvvm3Fxcebdd9/tacO1hRPx2WefmZ9++qm5efNm8/PPPzd79OhhnnrqqWZFRYVpmlxXOL7CwkJzxYoV5ooVK0xJ5tSpU80VK1aY27ZtM02zbtfQTTfdZKampppffPGFuXz5cvOMM84we/ToYVZVVdWrFoL3YcHb7XabDz30kJmUlGQ6nU5z0KBB5urVq+0rEF7v0ksvNZOTk82AgAAzJSXFHD16tLlmzRrPfq4pnKiPPvrIzMzMNJ1Op9mpUyfzpZdeqrGfawsnas6cOaYkc/369Ufs47rCiSgoKDBvu+02s3Xr1mZQUJDZrl0787777jPLy8s9bbi2cCLeeecds127dmZgYKCZlJRkjh8/3szLy/Ps57rC8Xz99dempCMeV199tWmadbuGSktLzZtvvtmMiYkxg4ODzfPOO8/cvn17vWsxTNM0G9ZJDwAAAAAAatNi7vEGAAAAAMAOBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAAACxE8AYAwMtMnDhRJ598st1lAACARkLwBgCgCRmGcczHNddcoz/96U/68ssv7S61hq1bt8owDK1cudLuUgAA8Dn+dhcAAEBLsmfPHs/X77zzjh588EGtX7/esy04OFhhYWEKCwuzozwAAGABerwBAGhCSUlJnkdkZKQMwzhi22+Hml9zzTUaNWqUJk2apMTEREVFRenhhx9WVVWV7rrrLsXExCg1NVX/+te/apxr165duvTSSxUdHa3Y2FhdeOGF2rp1a6215ebm6sorr1R8fLyCg4OVkZGhl19+WZKUnp4uSerZs6cMw9CQIUM8r3v55ZfVuXNnBQUFqVOnTnr++ec9+w71lM+aNUv9+/dXUFCQunbtqnnz5tXpvAAANAf0eAMA4AO++uorpaamasGCBfr222913XXX6bvvvtOgQYP0ww8/6J133tFNN92kESNGKC0tTSUlJRo6dKgGDhyoBQsWyN/fX48++qjOOuss/fjjjwoMDDziHA888IDWrl2rTz/9VHFxcdq0aZNKS0slSYsXL9app56qL774Ql27dvW8fubMmXrooYc0ffp09ezZUytWrNANN9yg0NBQXX311Z5j33XXXZo2bZq6dOmiqVOn6oILLtCWLVsUGxt7zPMCANAcELwBAPABMTExevbZZ+Xn56eTTjpJU6ZMUUlJif7yl79Iku699149/vjj+vbbb3XZZZdp1qxZ8vPz0z/+8Q8ZhiGpumc6KipK8+bN08iRI484x/bt29WzZ0/16dNHktS2bVvPvvj4eElSbGyskpKSPNv/+te/6umnn9bo0aMlVfeMr127Vi+++GKN4H3zzTfr4osvliTNmDFDn332mf75z3/q7rvvPuZ5AQBoDgjeAAD4gK5du8rP79c7xBITE5WZmel57nA4FBsbq5ycHEnSsmXLtGnTJoWHh9c4TllZmX7++eejnuOPf/yjLr74Yi1fvlwjR47UqFGj1L9//1pr2rdvn3bs2KHrrrtON9xwg2d7VVWVIiMja7Tt16+f52t/f3/16dNH69atO6HzAgDgawjeAAD4gICAgBrPDcM46ja32y1Jcrvd6t27t958880jjnWo9/q3zj77bG3btk3/+9//9MUXX2jYsGEaP368nnrqqaO2P3SumTNnqm/fvjX2ORyO435Ph3ri63teAAB8DZOrAQDQDPXq1UsbN25UQkKCOnToUOPx297ow8XHx+uaa67RG2+8oWnTpumll16SJM893S6Xy9M2MTFRrVq10ubNm484x6HJ2A75/vvvPV9XVVVp2bJl6tSp03HPCwBAc0CPNwAAzdCVV16pJ598UhdeeKEeeeQRpaamavv27Xr//fd11113KTU19YjXPPjgg+rdu7e6du2q8vJyffzxx+rcubMkKSEhQcHBwfrss8+UmpqqoKAgzwzst956qyIiInT22WervLxcS5cuVW5uru644w7Psf/+978rIyNDnTt31jPPPKPc3FyNHTv2uOcFAKA5oMcbAIBmKCQkRAsWLFDr1q01evRode7cWWPHjlVpaakiIiKO+prAwEDde++96t69uwYNGiSHw6FZs2ZJqr4v+9lnn9WLL76olJQUXXjhhZKk66+/Xv/4xz/0yiuvqFu3bho8eLBeeeWVI3q8H3/8cT3xxBPq0aOHvvnmG/33v/9VXFzccc8LAEBzYJimadpdBAAAaJ62bt2q9PR0rVixosba5AAAtCT0eAMAAAAAYCGCNwAAAAAAFmKoOQAAAAAAFqLHGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAAL/T/f+57qD/yYBQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,8))\n", + "\n", + "ax.bar(df.index, df['y'], color='lightgray')\n", + "ax.plot(cv_df.index, cv_df['CrostonClassic'], ls='--', label='Croston')\n", + "ax.set_ylabel('Value')\n", + "ax.set_xlabel('Time steps')\n", + "ax.legend(loc='best')\n", + "plt.xlim(40, 100)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "2389581d", + "metadata": {}, + "source": [ + "## Optimized Croston's Method " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d15babc7", + "metadata": {}, + "outputs": [], + "source": [ + "from statsforecast.models import CrostonOptimized\n", + "\n", + "models = [CrostonOptimized()]\n", + "\n", + "sf = StatsForecast(\n", + " df=df,\n", + " models=models,\n", + " freq='H',\n", + " n_jobs=-1\n", + ")\n", + "\n", + "cv_df = sf.cross_validation(\n", + " df=df,\n", + " h=1,\n", + " step_size=1,\n", + " n_windows=50\n", + ")\n", + "\n", + "cv_df.index = np.arange(50, 100, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f07a97af", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,8))\n", + "\n", + "ax.bar(df.index, df['y'], color='lightgray')\n", + "ax.plot(cv_df.index, cv_df['CrostonOptimized'], ls='--', label='Croston (optimized)')\n", + "ax.set_ylabel('Value')\n", + "ax.set_xlabel('Time steps')\n", + "ax.legend(loc='best')\n", + "plt.xlim(40, 100)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c116ec6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/26_croston_starter.ipynb b/26_croston_starter.ipynb new file mode 100644 index 0000000..93e2bb5 --- /dev/null +++ b/26_croston_starter.ipynb @@ -0,0 +1,88 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "81a34b98", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1cf585c", + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams[\"figure.figsize\"] = (9,6)" + ] + }, + { + "cell_type": "markdown", + "id": "97dfdc88", + "metadata": {}, + "source": [ + "# Croston's method" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67466e30", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('data/intermittent_time_series.csv')\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "2389581d", + "metadata": {}, + "source": [ + "## Optimized Croston's Method " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c116ec6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/27_adida_imapa.ipynb b/27_adida_imapa.ipynb new file mode 100644 index 0000000..e7d722c --- /dev/null +++ b/27_adida_imapa.ipynb @@ -0,0 +1,332 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "id": "6027e279", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from statsforecast import StatsForecast\n", + "from statsforecast.models import CrostonOptimized\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "89c57f05", + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams[\"figure.figsize\"] = (9,6)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "224ffed1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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unique_iddsy
012023-01-01 00:00:0043
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" + ], + "text/plain": [ + " unique_id ds y\n", + "0 1 2023-01-01 00:00:00 43\n", + "1 1 2023-01-01 01:00:00 87\n", + "2 1 2023-01-01 02:00:00 89\n", + "3 1 2023-01-01 03:00:00 87\n", + "4 1 2023-01-01 04:00:00 73" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('data/intermittent_time_series.csv')\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "df4c6ea3", + "metadata": {}, + "source": [ + "## ADIDA " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "58b6750d", + "metadata": {}, + "outputs": [], + "source": [ + "from statsforecast.models import ADIDA\n", + "\n", + "models = [CrostonOptimized(), ADIDA()]\n", + "\n", + "sf = StatsForecast(\n", + " df=df,\n", + " models=models,\n", + " freq='H',\n", + " n_jobs=-1\n", + ")\n", + "\n", + "cv_df = sf.cross_validation(\n", + " df=df,\n", + " h=1,\n", + " step_size=1,\n", + " n_windows=50\n", + ")\n", + "\n", + "cv_df.index = np.arange(50, 100, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "768d18c8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,8))\n", + "\n", + "ax.bar(df.index, df['y'], color='lightgray')\n", + "ax.plot(cv_df.index, cv_df['CrostonOptimized'], ls='--', label='Croston (optimized)')\n", + "ax.plot(cv_df.index, cv_df['ADIDA'], ls=':', label='ADIDA')\n", + "ax.set_ylabel('Value')\n", + "ax.set_xlabel('Time steps')\n", + "ax.legend(loc='best')\n", + "plt.xlim(40, 100)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "feed0476", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAE Croston: 31.68775177001953\n", + "MAE ADIDA: 30.451702117919922\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_absolute_error\n", + "\n", + "mae_croston = mean_absolute_error(cv_df['y'], cv_df['CrostonOptimized'])\n", + "mae_adida = mean_absolute_error(cv_df['y'], cv_df['ADIDA'])\n", + "\n", + "print(f'MAE Croston: {mae_croston}')\n", + "print(f'MAE ADIDA: {mae_adida}')" + ] + }, + { + "cell_type": "markdown", + "id": "f28706aa", + "metadata": {}, + "source": [ + "## IMAPA " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "8ed3259f", + "metadata": {}, + "outputs": [], + "source": [ + "from statsforecast.models import IMAPA\n", + "\n", + "models = [ADIDA(), IMAPA()]\n", + "\n", + "sf = StatsForecast(\n", + " df=df,\n", + " models=models,\n", + " freq='H',\n", + " n_jobs=-1\n", + ")\n", + "\n", + "cv_df = sf.cross_validation(\n", + " df=df,\n", + " h=1,\n", + " step_size=1,\n", + " n_windows=50\n", + ")\n", + "\n", + "cv_df.index = np.arange(50, 100, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "0e7710e0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,8))\n", + "\n", + "ax.bar(df.index, df['y'], color='lightgray')\n", + "ax.plot(cv_df.index, cv_df['IMAPA'], ls='--', label='IMAPA')\n", + "ax.plot(cv_df.index, cv_df['ADIDA'], ls=':', label='ADIDA')\n", + "ax.set_ylabel('Value')\n", + "ax.set_xlabel('Time steps')\n", + "ax.legend(loc='best')\n", + "plt.xlim(40, 100)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "87d6cbe5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAE Croston: 30.451702117919922\n", + "MAE ADIDA: 30.451702117919922\n" + ] + } + ], + "source": [ + "mae_imapa = mean_absolute_error(cv_df['y'], cv_df['IMAPA'])\n", + "mae_adida = mean_absolute_error(cv_df['y'], cv_df['ADIDA'])\n", + "\n", + "print(f'MAE Croston: {mae_imapa}')\n", + "print(f'MAE ADIDA: {mae_adida}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a98074fa", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/27_adida_imapa_starter.ipynb b/27_adida_imapa_starter.ipynb new file mode 100644 index 0000000..eac717a --- /dev/null +++ b/27_adida_imapa_starter.ipynb @@ -0,0 +1,196 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "6027e279", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from statsforecast import StatsForecast\n", + "from statsforecast.models import CrostonOptimized\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89c57f05", + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams[\"figure.figsize\"] = (9,6)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "224ffed1", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('data/intermittent_time_series.csv')\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "df4c6ea3", + "metadata": {}, + "source": [ + "## ADIDA " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8be5a99", + "metadata": {}, + "outputs": [], + "source": [ + "# Model with ADIDA and Croston" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "768d18c8", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10,8))\n", + "\n", + "ax.bar(df.index, df['y'], color='lightgray')\n", + "ax.plot(cv_df.index, cv_df['CrostonOptimized'], ls='--', label='Croston (optimized)')\n", + "ax.plot(cv_df.index, cv_df['ADIDA'], ls=':', label='ADIDA')\n", + "ax.set_ylabel('Value')\n", + "ax.set_xlabel('Time steps')\n", + "ax.legend(loc='best')\n", + "plt.xlim(40, 100)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "feed0476", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate the MAE\n", + "from sklearn.metrics import mean_absolute_error\n", + "\n", + "\n", + "print(f'MAE Croston: {mae_croston}')\n", + "print(f'MAE ADIDA: {mae_adida}')" + ] + }, + { + "cell_type": "markdown", + "id": "f28706aa", + "metadata": {}, + "source": [ + "## IMAPA " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ed3259f", + "metadata": {}, + "outputs": [], + "source": [ + "# Model with IMAPA and ADIDA\n", + "\n", + "sf = StatsForecast(\n", + " df=df,\n", + " models=models,\n", + " freq='H',\n", + " n_jobs=-1\n", + ")\n", + "\n", + "cv_df = sf.cross_validation(\n", + " df=df,\n", + " h=1,\n", + " step_size=1,\n", + " n_windows=50\n", + ")\n", + "\n", + "cv_df.index = np.arange(50, 100, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e7710e0", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10,8))\n", + "\n", + "ax.bar(df.index, df['y'], color='lightgray')\n", + "ax.plot(cv_df.index, cv_df['IMAPA'], ls='--', label='IMAPA')\n", + "ax.plot(cv_df.index, cv_df['ADIDA'], ls=':', label='ADIDA')\n", + "ax.set_ylabel('Value')\n", + "ax.set_xlabel('Time steps')\n", + "ax.legend(loc='best')\n", + "plt.xlim(40, 100)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87d6cbe5", + "metadata": {}, + "outputs": [], + "source": [ + "mae_imapa = mean_absolute_error(cv_df['y'], cv_df['IMAPA'])\n", + "mae_adida = mean_absolute_error(cv_df['y'], cv_df['ADIDA'])\n", + "\n", + "print(f'MAE Croston: {mae_imapa}')\n", + "print(f'MAE ADIDA: {mae_adida}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a98074fa", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/28_tsb.ipynb b/28_tsb.ipynb new file mode 100644 index 0000000..14fd06c --- /dev/null +++ b/28_tsb.ipynb @@ -0,0 +1,247 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "id": "ee2db499", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from statsforecast import StatsForecast\n", + "from statsforecast.models import CrostonOptimized, ADIDA\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b139d7c5", + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams[\"figure.figsize\"] = (9,6)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "33cddc77", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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unique_iddsy
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" + ], + "text/plain": [ + " unique_id ds y\n", + "0 1 2023-01-01 00:00:00 43\n", + "1 1 2023-01-01 01:00:00 87\n", + "2 1 2023-01-01 02:00:00 89\n", + "3 1 2023-01-01 03:00:00 87\n", + "4 1 2023-01-01 04:00:00 73" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('data/intermittent_time_series.csv')\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "4f154f55", + "metadata": {}, + "source": [ + "## TSB " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "86c9c8cc", + "metadata": {}, + "outputs": [], + "source": [ + "from statsforecast.models import TSB\n", + "\n", + "models = [TSB(0.1, 0.1), ADIDA(), CrostonOptimized()]\n", + "\n", + "sf = StatsForecast(\n", + " df=df,\n", + " models=models,\n", + " freq='H',\n", + " n_jobs=-1\n", + ")\n", + "\n", + "cv_df = sf.cross_validation(\n", + " df=df,\n", + " h=1,\n", + " step_size=1,\n", + " n_windows=50\n", + ")\n", + "\n", + "cv_df.index = np.arange(50, 100, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "9520e958", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,8))\n", + "\n", + "ax.bar(df.index, df['y'], color='lightgray')\n", + "ax.plot(cv_df.index, cv_df['TSB'], ls='--', label='TSB')\n", + "ax.plot(cv_df.index, cv_df['CrostonOptimized'], ls=':', label='Croston')\n", + "ax.plot(cv_df.index, cv_df['ADIDA'], ls='-.', label='ADIDA')\n", + "\n", + "ax.set_ylabel('Value')\n", + "ax.set_xlabel('Time steps')\n", + "ax.legend(loc='best')\n", + "plt.xlim(40, 100)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "36c5e95f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAE Croston: 31.68775177001953\n", + "MAE ADIDA: 30.451702117919922\n", + "MAE TSB: 30.650671005249023\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_absolute_error\n", + "\n", + "mae_croston = mean_absolute_error(cv_df['y'], cv_df['CrostonOptimized'])\n", + "mae_adida = mean_absolute_error(cv_df['y'], cv_df['ADIDA'])\n", + "mae_tsb = mean_absolute_error(cv_df['y'], cv_df['TSB'])\n", + "\n", + "print(f'MAE Croston: {mae_croston}')\n", + "print(f'MAE ADIDA: {mae_adida}')\n", + "print(f'MAE TSB: {mae_tsb}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51ea3a57", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/28_tsb_starter.ipynb b/28_tsb_starter.ipynb new file mode 100644 index 0000000..e93b4a1 --- /dev/null +++ b/28_tsb_starter.ipynb @@ -0,0 +1,149 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "ee2db499", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from statsforecast import StatsForecast\n", + "from statsforecast.models import CrostonOptimized, ADIDA\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b139d7c5", + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams[\"figure.figsize\"] = (9,6)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33cddc77", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('data/intermittent_time_series.csv')\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "4f154f55", + "metadata": {}, + "source": [ + "## TSB " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86c9c8cc", + "metadata": {}, + "outputs": [], + "source": [ + "# Model with Croston, ADIDA and TSB\n", + "\n", + "sf = StatsForecast(\n", + " df=df,\n", + " models=models,\n", + " freq='H',\n", + " n_jobs=-1\n", + ")\n", + "\n", + "cv_df = sf.cross_validation(\n", + " df=df,\n", + " h=1,\n", + " step_size=1,\n", + " n_windows=50\n", + ")\n", + "\n", + "cv_df.index = np.arange(50, 100, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9520e958", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10,8))\n", + "\n", + "ax.bar(df.index, df['y'], color='lightgray')\n", + "ax.plot(cv_df.index, cv_df['TSB'], ls='--', label='TSB')\n", + "ax.plot(cv_df.index, cv_df['CrostonOptimized'], ls=':', label='Croston')\n", + "ax.plot(cv_df.index, cv_df['ADIDA'], ls='-.', label='ADIDA')\n", + "\n", + "ax.set_ylabel('Value')\n", + "ax.set_xlabel('Time steps')\n", + "ax.legend(loc='best')\n", + "plt.xlim(40, 100)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36c5e95f", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import mean_absolute_error\n", + "\n", + "mae_croston = mean_absolute_error(cv_df['y'], cv_df['CrostonOptimized'])\n", + "mae_adida = mean_absolute_error(cv_df['y'], cv_df['ADIDA'])\n", + "mae_tsb = mean_absolute_error(cv_df['y'], cv_df['TSB'])\n", + "\n", + "print(f'MAE Croston: {mae_croston}')\n", + "print(f'MAE ADIDA: {mae_adida}')\n", + "print(f'MAE TSB: {mae_tsb}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51ea3a57", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/29_intermittent_error_metrics.ipynb b/29_intermittent_error_metrics.ipynb new file mode 100644 index 0000000..1f90c6b --- /dev/null +++ b/29_intermittent_error_metrics.ipynb @@ -0,0 +1,397 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "id": "a7722505", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from statsforecast import StatsForecast\n", + "from statsforecast.models import CrostonOptimized, ADIDA, TSB\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a9c10d95", + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams[\"figure.figsize\"] = (9,6)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "501318a4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,8))\n", + "\n", + "ax.bar(df.index, df['y'], color='lightgray')\n", + "ax.plot(cv_df.index, cv_df['TSB'], ls='--', label='TSB')\n", + "ax.plot(cv_df.index, cv_df['CrostonOptimized'], ls=':', label='Croston')\n", + "ax.plot(cv_df.index, cv_df['ADIDA'], ls='-.', label='ADIDA')\n", + "\n", + "ax.set_ylabel('Value')\n", + "ax.set_xlabel('Time steps')\n", + "ax.legend(loc='best')\n", + "plt.xlim(40, 100)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "d1459658", + "metadata": {}, + "source": [ + "## CFE, CFE_min, CFE_max, PIS, NOS" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "dcc24fb9", + "metadata": {}, + "outputs": [], + "source": [ + "def errors(y_true, y_pred):\n", + " \n", + " # CFE\n", + " cfe_all = np.cumsum(y_true - y_pred)\n", + " cfe = cfe_all.iloc[-1]\n", + " \n", + " cfe_max = np.max(cfe_all)\n", + " cfe_min = np.min(cfe_all)\n", + " \n", + " # PIS\n", + " pis_all = -np.cumsum(cfe_all)\n", + " pis = pis_all.iloc[-1]\n", + " \n", + " # NOS\n", + " nos = len(cfe_all[cfe_all > 0])\n", + "\n", + " errors = {\n", + " \"CFE\": cfe,\n", + " \"CFE_max\": cfe_max,\n", + " \"CFE_min\": cfe_min,\n", + " \"PIS\": pis,\n", + " \"NOS\": nos\n", + " }\n", + " \n", + " return errors" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "312c4b2d", + "metadata": {}, + "outputs": [], + "source": [ + "croston_errors = errors(cv_df['y'], cv_df['CrostonOptimized'])" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "9dd24bdd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'CFE': -14.413212,\n", + " 'CFE_max': 34.96125411987305,\n", + " 'CFE_min': -332.0770568847656,\n", + " 'PIS': 5527.8726,\n", + " 'NOS': 3}" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "croston_errors" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "66468c6f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'CFE': 39.358646,\n", + " 'CFE_max': 92.94744873046875,\n", + " 'CFE_min': -259.12799072265625,\n", + " 'PIS': 3711.2573,\n", + " 'NOS': 8}" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adida_errors = errors(cv_df['y'], cv_df['ADIDA'])\n", + "adida_errors" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "b15a2443", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'CFE': 5.303444,\n", + " 'CFE_max': 57.37592315673828,\n", + " 'CFE_min': -291.7810363769531,\n", + " 'PIS': 4825.8857,\n", + " 'NOS': 6}" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tsb_errors = errors(cv_df['y'], cv_df['TSB'])\n", + "tsb_errors" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "0d266cfb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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xx/Tp0ydbbrll7rjjjgwdOnSFx37uuefy4Ycf5sUXX8yFF16YrbbaKsccc8xanYfIAwAASPLevBUH3tqMSz79YpUdd9wxU6ZMWem4bt26Jfk0DN99991ccsklax15X9vbNW+66aZ069YtG264YXbbbbc899xzjT0lAACgEW3aesN1Oi5J6uvr8/rrr6djx1V/YctSpVIp9fX1qx64Al/LyPvFL36R8847Lz/60Y/y6quvZu+9985BBx2UadOmNfbUAACARrJHt3bpWLNhVvSghIokHWs2zB7d2q3wGBdccEGeeeaZvP322/nd736X73znO5k7d25OPPHEJMmwYcNywgknlMffeOONeeihhzJlypRMmTIlt99+e66++uocf/zxa30eX8vIGzVqVE455ZSceuqp2X777XPdddelrq4uo0ePbuypAQAAjaRyg4oMP6RHkiwTekuXhx/SY6XPy3vnnXdyzDHHZNttt83hhx+eqqqqvPjii+natWuSZObMmQ0uLi1ZsiTDhg3LLrvskt69e+f666/PFVdckREjRqz1eXztPpO3cOHCjB8/PhdeeGGD9QceeGCef/755e5TX1/f4HLp3Llz1+scAQCAxjGgZ8eMPn7XDH/wtQaPUahdzefk3XvvvSvdPmbMmAbL55xzTs4555y1nu/yfO2u5P3tb3/L4sWLs9lmmzVYv9lmm2XWrFnL3efyyy9PTU1N+VVXV/dlTBUAAGgEA3p2zBND//9z6sactHvG/vAfVutB6F8FX7vIW6qiouEl1lKptMy6pYYNG5Y5c+aUX9OnT/8ypggAADSSz96SuUe3diu9RfOr5mt3u2aHDh1SWVm5zFW79957b5mre0tVV1enurr6y5geAADAF/K1u5JXVVWV3XbbLY8//niD9Y8//nj22muvRpoVAADAuvG1u5KXJEOHDs2gQYPSu3fv9OnTJ7fcckumTZuWM844o7GnBgAA8IV8LSPvqKOOyvvvv58RI0Zk5syZ6dmzZ37zm9+Uv9YUAACgqfpaRl6SDBkyJEOGDGnsaQAAAKxTX7vP5AEAABSZyAMAACgQkUfZ/fffn/79+6dDhw6pqKjIhAkTVji2VCrloIMOSkVFRX71q1+t9ntcfvnlqaioyHnnnbfW7w0AAKyYyGviFi8plf/80tsfNFheU/Pnz0/fvn1zxRVXrHLsddddt8KHx6/IuHHjcsstt2SnnXb6Qu8NAACs2Nf2i1eK4JFJMzP8wdfKy4NvH5eONRtm+CE9MqBnxzU+3qBBg5IkU6dOXem43//+9xk1alTGjRuXjh1X730+/PDDHHfccbn11lvzP/7H/1jr9wYAAFbOlbwm6pFJM3Pm3a/k3bn1DdbPmrMgZ979Sh6ZNHO9vO9HH32UY445JjfccENqa2tXe7+zzjorAwcOzP77779e5gUAAHzKlbwmaPGSUi59aHKWd2NmKUlFkksfmpwDetSmcoM1u6VyVb73ve9lr732yre//e3V3ufee+/NK6+8knHjxq3TuQAAAMtyJa8JeuntDzJzzoIVbi8lmTlnQV56+4MVjrnnnnuy0UYblV/PPffcKt/3wQcfzG9/+9tcd911qz3X6dOn57vf/W7uvvvubLjhhqu9HwAAsHZcyWuC3pu34sBb3XGHHnpo9txzz/Jy586dV3m83/72t3nrrbey8cYbN1j/T//0T9l7773z9NNPL7PP+PHj895772W33XYrr1u8eHGeffbZ3HDDDamvr09lZeWqTwYAAFgtIq8J2rT16l0RW9m41q1bp3Xr1mv0vhdeeGFOPfXUBut23HHHXHvttTnkkEOWu89+++2XiRMnNlh30kknZbvttssPf/hDgQcAAOuYyGuC9ujWLh1rNsysOQuW+7m8iiS1NRtmj27t1ui4H3zwQaZNm5YZM2YkSd54440kSW1tbYPX53Xp0iXdunUrL++33375x3/8x5x99tlp3bp1evbs2WB8q1at0r59+wbrV/XeAADA6vGZvCaocoOKDD+kR5JPg+6zli4PP6THGn/pyoMPPphevXpl4MCBSZKjjz46vXr1yk9/+tM1Os5bb72Vv/3tb43y3gAA8HVXUSqV1v7p2V9Tc+fOTU1NTebMmZM2bdo02jyWPifvs49R+CLPyQNg/fpo4aL0uPjRJMnkEf3TssoNNQBfVV/F39mr2yGNP1PW2oCeHdN3qw7Z8ZLHkiRjTto9e2+9yTp/bAIAANB0uF2zifts0O3RrZ3AAwCArzmRBwAAUCAiDwAAoEBEHgAAQIGIPAAAgAIReQAAAAUi8gAAAApE5AEAABSIyAMAACgQkQcAAFAgIg8AAKBARB4AAECBiDwAAIACEXkAAAAFIvIAAAAKROQBAAAUiMgDAAAoEJEHAABQICIPAACgQEQeAABAgYg8AACAAhF5AAAABSLyAAAACkTkAQAAFIjIAwAAKBCRBwAAUCAiDwAAoEBEHgAAQIGIPAAAgAIReQAAAAUi8gAAAApE5AEAABSIyAMAACgQkQcAAFAgIg8AAKBARB4AAECBiDwAAIACEXkAAAAFIvIAAAAKROQBAAAUiMgDAAAoEJEHAABQICIPAACgQEQeAABAgYg8AACAAhF5AAAABSLyAAAACkTkAQAAFIjIAwAAKBCRBwAAUCAiDwAAoEBEHgAAQIGIPAAAgAIReQAAAAUi8gAAAApE5AEAABSIyAMAACgQkQcAAFAgIg8AAKBARB4AAECBiDwAAIACEXkAAAAFIvIAAAAKROQBAAAUiMgDAAAoEJEHAABQICIPAACgQEQeAABAgYg8AACAAhF5AAAABSLyAAAACkTkAQAAFIjIAwAAKBCRBwAAUCAiDwAAoECaTORddtll2WuvvdKyZctsvPHGyx0zbdq0HHLIIWnVqlU6dOiQc889NwsXLmwwZuLEienXr19atGiRzp07Z8SIESmVSl/CGQAAAKx/axR5V155ZT7++OPy8rPPPpv6+vry8rx58zJkyJB1N7vPWLhwYY444oiceeaZy92+ePHiDBw4MPPnz8/YsWNz77335r777sv5559fHjN37twccMAB6dSpU8aNG5frr78+V199dUaNGrVe5gwAAPBlW6PIGzZsWObNm1dePvjgg/PXv/61vPzRRx/l5ptvXnez+4xLL7003/ve97Ljjjsud/tjjz2WyZMn5+67706vXr2y//7755prrsmtt96auXPnJknuueeeLFiwIGPGjEnPnj1z+OGH56KLLsqoUaNczQMAAAphjSLv8yH0VQqjF154IT179kynTp3K6/r375/6+vqMHz++PKZfv36prq5uMGbGjBmZOnXqCo9dX1+fuXPnNngBAAB8FTWZz+StyqxZs7LZZps1WNe2bdtUVVVl1qxZKxyzdHnpmOW5/PLLU1NTU37V1dWt49kDAACsG40aeZdcckkqKipW+nr55ZdX+3gVFRXLrCuVSg3Wf37M0quRy9t3qWHDhmXOnDnl1/Tp01d7TgAAAF+mZmu6w7/9279lo402SpIsWrQoY8aMSYcOHZKkwef1VsfZZ5+do48+eqVjtthii9U6Vm1tbX73u981WDd79ux88skn5at1tbW1y1yxe++995JkmSt8n1VdXd3gFk8AAICvqjWKvC5duuTWW28tL9fW1uauu+5aZszq6tChQzkQv6g+ffrksssuy8yZM9OxY8ckn34ZS3V1dXbbbbfymIsuuigLFy5MVVVVeUynTp1WOyYBAAC+ytYo8lb25STr27Rp0/LBBx9k2rRpWbx4cSZMmJAk2WqrrbLRRhvlwAMPTI8ePTJo0KBcddVV+eCDD3LBBRfktNNOS5s2bZIkxx57bC699NIMHjw4F110UaZMmZKRI0fm4osvXuntmgAAAE3FGt+u2Vguvvji3HHHHeXlXr16JUmeeuqp7LPPPqmsrMzDDz+cIUOGpG/fvmnRokWOPfbYXH311eV9ampq8vjjj+ess85K796907Zt2wwdOjRDhw790s8HAABgfVjj2zVfffXVtG/fPklyww035IQTTihfKVufxowZkzFjxqxyfr/+9a9XOmbHHXfMs88+uw5nBgAA8NWxRt+u+c4772Tx4sXl5Ysuuih/+9vf1vmkAAAAWDtf6BEKX6WHoQMAAFCgh6EDAACwjp+Tt9S55567bmYHAADAGlnnz8mrqKgQeQAAAI2kyTwnDwAAgFVbo8/k/fa3v02PHj0yd+7cZbbNmTMnO+ywQ5577rl1NjkAAADWzBpF3nXXXZfTTjttuc/Fq6mpyemnn55Ro0ats8kBAACwZtYo8n7/+99nwIABK9x+4IEHZvz48V94UgAAAKydNYq8d999N82bN1/h9mbNmuW//uu/vvCkAAAAWDtrFHmdO3fOxIkTV7j9D3/4Qzp27PiFJwUAAMDaWaPI+9a3vpWLL744CxYsWGbbxx9/nOHDh+fggw9eZ5MDAABgzazRIxT++3//77n//vuzzTbb5Oyzz862226bioqKvP7667nxxhuzePHi/OhHP1pfcwUAAGAV1ijyNttsszz//PM588wzM2zYsJRKpSSfPgC9f//+uemmm7LZZputl4kCAACwamsUeUnStWvX/OY3v8ns2bPz5ptvplQqZeutt07btm3Xx/wAAABYA2sceUu1bds2u++++7qcCwAAAF/QGn3xCgAAAF9tIg8AAKBARB4AAECBiDwAAIACEXkAAAAFIvIAAAAKROQBAAAUiMgDAAAoEJEHAABQICIPAACgQEQeAABAgYg8AACAAhF5AAAABSLyAAAACkTkAQAAFIjIAwAAKBCRBwAAUCAiDwAAoEBEHgAAQIGIPAAAgAIReQAAAAUi8gAAAApE5AEAABSIyAMAACgQkQcAAFAgIg8AAKBARB4AAECBiDwAAIACEXkAAAAFIvIAAAAKROQBAAAUiMgDAAAoEJEHAABQICIPAACgQEQeAABAgYg8AACAAhF5AAAABSLyAAAACkTkAQAAFIjIAwAAKBCRBwAAUCAiDwAAoEBEHgAAQIGIPAAAgAIReQAAAAUi8gAAAApE5AEAABSIyAMAACgQkQcAAFAgIg8AAKBARB4AAECBiDwAAIACEXkAAAAFIvIAAAAKROQBAAAUiMgDAAAoEJEHAABQICIPAACgQEQeAABAgYg8AACAAhF5AAAABSLyAAAACkTkAQAAFIjIAwAAKBCRBwAAUCAiDwAAoEBEHgAAQIGIPAAAgAIReQAAAAUi8gAAAApE5AEAABSIyAMAACgQkQcAAFAgTSLypk6dmlNOOSXdunVLixYtsuWWW2b48OFZuHBhg3HTpk3LIYccklatWqVDhw4599xzlxkzceLE9OvXLy1atEjnzp0zYsSIlEqlL/N0AAAA1ptmjT2B1fHHP/4xS5Ysyc0335ytttoqkyZNymmnnZb58+fn6quvTpIsXrw4AwcOzCabbJKxY8fm/fffz4knnphSqZTrr78+STJ37twccMAB2XfffTNu3Lj86U9/yuDBg9OqVaucf/75jXmKAAAA60STiLwBAwZkwIAB5eXu3bvnjTfeyOjRo8uR99hjj2Xy5MmZPn16OnXqlCS55pprMnjw4Fx22WVp06ZN7rnnnixYsCBjxoxJdXV1evbsmT/96U8ZNWpUhg4dmoqKikY5PwAAgHWlSdyuuTxz5sxJu3btyssvvPBCevbsWQ68JOnfv3/q6+szfvz48ph+/fqlurq6wZgZM2Zk6tSpK3yv+vr6zJ07t8ELAADgq6hJRt5bb72V66+/PmeccUZ53axZs7LZZps1GNe2bdtUVVVl1qxZKxyzdHnpmOW5/PLLU1NTU37V1dWtq1MBAABYpxo18i655JJUVFSs9PXyyy832GfGjBkZMGBAjjjiiJx66qkNti3vdstSqdRg/efHLP3SlZXdqjls2LDMmTOn/Jo+ffoanysAAMCXoVE/k3f22Wfn6KOPXumYLbbYovznGTNmZN99902fPn1yyy23NBhXW1ub3/3udw3WzZ49O5988kn5al1tbe0yV+zee++9JFnmCt9nVVdXN7jFEwAA4KuqUSOvQ4cO6dChw2qN/etf/5p99903u+22W26//fZssEHDi5B9+vTJZZddlpkzZ6Zjx45JPv0ylurq6uy2227lMRdddFEWLlyYqqqq8phOnTo1iEkAAICmqkl8Jm/GjBnZZ599UldXl6uvvjr/9V//lVmzZjW4KnfggQemR48eGTRoUF599dU8+eSTueCCC3LaaaelTZs2SZJjjz021dXVGTx4cCZNmpQHHnggI0eO9M2aAABAYTSJRyg89thjefPNN/Pmm29m8803b7Bt6WfqKisr8/DDD2fIkCHp27dvWrRokWOPPbb8iIUkqampyeOPP56zzjorvXv3Ttu2bTN06NAMHTr0Sz0fAACA9aVJRN7gwYMzePDgVY7r0qVLfv3rX690zI477phnn312Hc0MAADgq6VJ3K4JAADA6hF5AAAABSLyAAAACkTkAQAAFIjIAwAAKBCRBwAAUCAiDwAAoEBEHgAAQIGIPAAAgAIReQAAAAUi8gAAAApE5AEAABSIyAMAACiQZo09AQD4umhZ1SxTrxjY2NMAoOBcyQMAACgQkQcAAFAgIg8AAKBARB4AAECBiDwAAIACEXkAAAAFIvIAAAAKROQBAAAUiMgDAAAoEJEHAABQICIPAACgQEQeAABAgYg8AACAAhF5AAAABSLyAAAACkTkAQAAFIjIAwAAKBCRBwAAUCAiDwAAoEBEHgAAQIGIPAAAgAIReQAAAAUi8gAAAApE5AEAABSIyAMAACgQkQcAAFAgIg8AAKBARB4AAECBiDwAAIACEXkAAAAFIvIAAAAKROQBAAAUiMgDAAAoEJEHAABQICIPAACgQEQeAABAgYg8AACAAhF5AAAABSLyAAAACkTkAQAAFIjIAwAAKBCRBwAAUCAiDwAAoEBEHgAAQIGIPAAAgAIReQAAAAUi8gAAAApE5AEAABSIyAMAACgQkQcAAFAgIg8AAKBARB4AAECBiDwAAIACEXkAAAAFIvIAAAAKROQBAAAUiMgDAAAoEJEHAABQICIPAACgQEQeAABAgYg8AACAAhF5AAAABSLyAAAACkTkAQAAFIjIAwAAKBCRBwAAUCDNGnsCAAAAXzUtq5pl6hUDG3saa8WVPAAAgAIReQAAAAUi8gAAAApE5AEAABSIyAMAACgQkQcAAFAgIg8AAKBARB4AAECBNJnIO/TQQ9OlS5dsuOGG6dixYwYNGpQZM2Y0GDNt2rQccsghadWqVTp06JBzzz03CxcubDBm4sSJ6devX1q0aJHOnTtnxIgRKZVKX+apAAAArDdNJvL23Xff/O///b/zxhtv5L777stbb72V73znO+XtixcvzsCBAzN//vyMHTs29957b+67776cf/755TFz587NAQcckE6dOmXcuHG5/vrrc/XVV2fUqFGNcUoAAADrXLPGnsDq+t73vlf+c9euXXPhhRfmsMMOyyeffJLmzZvnsccey+TJkzN9+vR06tQpSXLNNddk8ODBueyyy9KmTZvcc889WbBgQcaMGZPq6ur07Nkzf/rTnzJq1KgMHTo0FRUVjXV6AAAA60STuZL3WR988EHuueee7LXXXmnevHmS5IUXXkjPnj3LgZck/fv3T319fcaPH18e069fv1RXVzcYM2PGjEydOnWF71dfX5+5c+c2eAEAAHwVNanI++EPf5hWrVqlffv2mTZtWv793/+9vG3WrFnZbLPNGoxv27ZtqqqqMmvWrBWOWbq8dMzyXH755ampqSm/6urq1tUpAQAArFONGnmXXHJJKioqVvp6+eWXy+O///3v59VXX81jjz2WysrKnHDCCQ2+NGV5t1uWSqUG6z8/Zun+K7tVc9iwYZkzZ075NX369LU+ZwAAgPWpUT+Td/bZZ+foo49e6Zgtttii/OcOHTqkQ4cO2WabbbL99tunrq4uL774Yvr06ZPa2tr87ne/a7Dv7Nmz88knn5Sv1tXW1i5zxe69995LkmWu8H1WdXV1g1s8AQAAvqoaNfKWRtvaWHoFrr6+PknSp0+fXHbZZZk5c2Y6duyYJHnsscdSXV2d3XbbrTzmoosuysKFC1NVVVUe06lTpwYxCQAA0FQ1ic/kvfTSS7nhhhsyYcKE/OUvf8lTTz2VY489NltuuWX69OmTJDnwwAPTo0ePDBo0KK+++mqefPLJXHDBBTnttNPSpk2bJMmxxx6b6urqDB48OJMmTcoDDzyQkSNH+mZNAACgMJpE5LVo0SL3339/9ttvv2y77bY5+eST07NnzzzzzDPl2ygrKyvz8MMPZ8MNN0zfvn1z5JFH5rDDDsvVV19dPk5NTU0ef/zxvPPOO+ndu3eGDBmSoUOHZujQoY11agAAAOtURemz31zCapk7d25qamoyZ86c8lXCxvLRwkXpcfGjSZLJI/qnZVWTefQhAACwBla3Q5rElTwAAABWj8gDAAAoEJEHAABQICIPAACgQEQeAABAgYg8AACAAhF5AAAABSLyAAAACkTkAQAAFIjIAwAAKBCRBwAAUCAiDwAAoEBEHgAAQIGIPAAAgAIReQAAAAUi8gAAAApE5AEAABSIyAMAACgQkQcAAFAgIg8AAKBARB4AAECBiDwAAIACEXkAAAAFIvIAAAAKROQBAAAUiMgDAAAoEJEHAABQICIPAACgQEQeAABAgYg8AACAAhF5AAAABSLyAAAACkTkAQAAFIjIAwAAKBCRBwAAUCAiDwAAoEBEHgAAQIGIPAAAgAIReQAAAAUi8gAAAAqkWWNPgC+mZVWzTL1iYGNPAwAA+IpwJQ8AAKBARB4AAECBiDwAAIACEXkAAAAFIvIAAAAKROQBAAAUiMgDAAAoEJEHAABQICIPAACgQEQeAABAgYg8AACAAhF5AAAABSLyAAAACkTkAQAAFIjIAwAAKBCRBwAAUCAiDwAAoEBEHgAAQIGIPAAAgAIReQAAAAUi8gAAAApE5AEAABRIs8aeQFNUKpWSJHPnzm3kmQAAAF8XS/tjaY+siMhbC/PmzUuS1NXVNfJMAACAr5t58+alpqZmhdsrSqvKQJaxZMmSzJgxI61bt05FRUVjTydz585NXV1dpk+fnjZt2jT2dABYCb+zAZqOr9rv7FKplHnz5qVTp07ZYIMVf/LOlby1sMEGG2TzzTdv7Gkso02bNl+Jf/kAWDW/swGajq/S7+yVXcFbyhevAAAAFIjIAwAAKBCRVwDV1dUZPnx4qqurG3sqAKyC39kATUdT/Z3ti1cAAAAKxJU8AACAAhF5AAAABSLyAAAACkTkfUlmzZqVc845J927d091dXXq6upyyCGH5Mknn1xv77nFFlvkuuuuW2/HB/g6ef7551NZWZkBAwY0WD916tRUVFSUX61bt84OO+yQs846K1OmTGkwdsyYMdl4440bLC/dr7KyMm3bts2ee+6ZESNGZM6cOcudx8iRI1NZWZkrrrhinZ8jQJF99nf18l6DBw9Okjz11FPZd999065du7Rs2TJbb711TjzxxCxatChJ8vTTTzfYr0WLFtlhhx1yyy23NOLZNSTyvgRTp07Nbrvtlt/+9re58sorM3HixDzyyCPZd999c9ZZZy13n08++eRLniUAK/Ozn/0s55xzTsaOHZtp06Yts/2JJ57IzJkz8/vf/z4jR47M66+/np133nmVf5nXpk2bzJw5M++8806ef/75/PM//3PuvPPO7LLLLpkxY8Yy42+//fb84Ac/yM9+9rN1dm4AXwczZ84sv6677rry79+lr3/913/Na6+9loMOOii77757nn322UycODHXX399mjdvniVLljQ43htvvJGZM2dm8uTJOf3003PmmWeu1ws4a6TEenfQQQeVOnfuXPrwww+X2TZ79uxSqVQqJSmNHj26dOihh5ZatmxZuvjii0ulUql00003lbp3715q3rx5aZtttindeeedDfYfPnx4qa6urlRVVVXq2LFj6ZxzzimVSqVSv379SkkavJb65S9/WerRo0epqqqq1LVr19LVV1/d4Jhdu3YtXXbZZaWTTjqptNFGG5Xq6upKN99887r8kQA0KR9++GGpdevWpT/+8Y+lo446qnTppZeWt7399tulJKVXX321wT6LFy8u7bPPPqWuXbuWFi1aVCqVSqXbb7+9VFNTUx7z+eWl3n333VKHDh1Kxx13XIP1Tz/9dKlz586lhQsXljp16lR65pln1tk5AnydrOj377XXXlvaYostVrrvU089VUpS/v/4pbp371668sor1+Es154reevZBx98kEceeSRnnXVWWrVqtcz2z962M3z48Hz729/OxIkTc/LJJ+eBBx7Id7/73Zx//vmZNGlSTj/99Jx00kl56qmnkiS//OUvc+211+bmm2/OlClT8qtf/So77rhjkuT+++/P5ptvnhEjRpT/diJJxo8fnyOPPDJHH310Jk6cmEsuuSQ//vGPM2bMmAbzuuaaa9K7d++8+uqrGTJkSM4888z88Y9/XD8/JICvuF/84hfZdttts+222+b444/P7bffntIqnkC0wQYb5Lvf/W7+8pe/ZPz48Wv0fptuummOO+64PPjgg1m8eHF5/W233ZZjjjkmzZs3zzHHHJPbbrttrc4HgOWrra3NzJkz8+yzz672PqVSKY888kimT5+ePffccz3ObvU1a+wJFN2bb76ZUqmU7bbbbpVjjz322Jx88skNlgcPHpwhQ4YkSYYOHZoXX3wxV199dfbdd99MmzYttbW12X///dO8efN06dIle+yxR5KkXbt2qaysTOvWrVNbW1s+5qhRo7Lffvvlxz/+cZJkm222yeTJk3PVVVeV70NOkm9961vl9/3hD3+Ya6+9Nk8//fRqnQdA0dx22205/vjjkyQDBgzIhx9+mCeffDL777//Svdb+jtz6tSp5d/Pq2u77bbLvHnz8v7772fTTTfN3Llzc9999+X5559Pkhx//PHp27dvrr/++rRp02YtzgqAzzviiCPy6KOPpl+/fqmtrc03vvGN7LfffjnhhBOW+V27+eabJ0nq6+uzZMmSjBgxIt/85jcbY9rLcCVvPVv6N70VFRWrHNu7d+8Gy6+//nr69u3bYF3fvn3z+uuvJ/n0X8KPP/443bt3z2mnnZYHHnig/IHQFVnRMadMmdLgb4t32mmn8p8rKipSW1ub9957b5XnAFA0b7zxRl566aUcffTRSZJmzZrlqKOOWq3PxK3JfwNWte/Pf/7zdO/ePTvvvHOSZJdddkn37t1z7733rvGxAVi+ysrK3H777XnnnXdy5ZVXplOnTrnsssuyww47lO+MW+q5557LhAkTMmHChPzbv/1bRo4cmdGjRzfSzBsSeevZ1ltvnYqKinKYrczybuf8/P8YlEql8rq6urq88cYbufHGG9OiRYsMGTIk3/zmN1f6pS2f3f+z6z6vefPmy8zj8x82Bfg6uO2227Jo0aJ07tw5zZo1S7NmzTJ69Ojcf//9mT179kr3Xfq7v1u3bmv8vq+//nratGmT9u3bJ/n0i19ee+218hyaNWuW1157zS2bAOtB586dM2jQoNx4442ZPHlyFixYkJ/+9KcNxnTr1i1bbbVVdthhh5x00kkZNGhQLrvsskaacUMibz1r165d+vfvnxtvvDHz589fZvvf//73Fe67/fbbZ+zYsQ3WPf/889l+++3Lyy1atMihhx6an/zkJ3n66afzwgsvZOLEiUmSqqqqBlfnkqRHjx7LPeY222yTysrKNT09gEJbtGhR7rzzzlxzzTXlv62dMGFCfv/736dr16655557VrjvkiVL8pOf/CTdunVLr1691uh933vvvfz85z/PYYcdlg022CATJ07Myy+/nKeffrrBPJ599tmMGzcukyZN+qKnCsAKtG3bNh07dlzu/8t/VmVlZT7++OMvaVYr5zN5X4Kbbrope+21V/bYY4+MGDEiO+20UxYtWpTHH388o0ePXuFVvu9///s58sgjs+uuu2a//fbLQw89lPvvvz9PPPFEkk+fr7R48eLsueeeadmyZe666660aNEiXbt2TfLpc/KeffbZHH300amurk6HDh1y/vnnZ/fdd8+//Mu/5KijjsoLL7yQG264ITfddNOX9vMAaCp+/etfZ/bs2TnllFNSU1PTYNt3vvOd3HbbbTn44IOTJO+//35mzZqVjz76KJMmTcp1112Xl156KQ8//PBK/xKtVCpl1qxZKZVK+fvf/54XXnghI0eOTE1NTflZeLfddlv22GOP5X7Wo0+fPrntttty7bXXrsMzB/h6uvnmmzNhwoT84z/+Y7bccsssWLAgd955Z1577bVcf/31Dca+9957WbBgQerr6/PSSy/lrrvuyne+851GmvnnNNK3en7tzJgxo3TWWWeVunbtWqqqqip17ty5dOihh5aeeuqpUqn06SMUHnjggWX2W9kjFB544IHSnnvuWWrTpk2pVatWpW984xulJ554orz9hRdeKO20006l6urq5T5CoXnz5qUuXbqUrrrqqgbv2bVr19K1117bYN3OO+9cGj58+Bf+OQA0JQcffHDpW9/61nK3jR8/vpSk/M+lr5YtW5a233770pAhQ0pTpkxpsM/yHqGwdL+KiopSTU1NaY899iiNGDGiNGfOnFKpVCrV19eX2rdvv8Kv5b7mmmtKHTp0KNXX16+bkwb4GljRIxReeeWV0vHHH1/q1q1bqbq6utS+ffvSN7/5zdKDDz5YHrP0EQpLX82aNSt169atdMEFFyz3kWmNoaJUWsV3QAMAANBk+EweAABAgYg8AACAAhF5AAAABSLyAAAACkTkAQAAFIjIAwAAKBCRBwAAUCAiDwAAoEBEHgA0gqeffjoVFRX5+9//vtr7bLHFFrnuuuvW25wAKAaRBwDLMXjw4FRUVOSMM85YZtuQIUNSUVGRwYMHf/kTA4BVEHkAsAJ1dXW599578/HHH5fXLViwIP/rf/2vdOnSpRFnBgArJvIAYAV23XXXdOnSJffff3953f3335+6urr06tWrvK6+vj7nnntuNt1002y44Yb5b//tv2XcuHENjvWb3/wm22yzTVq0aJF99903U6dOXeb9nn/++Xzzm99MixYtUldXl3PPPTfz589f4fwuueSSdOnSJdXV1enUqVPOPffcL37SADR5Ig8AVuKkk07K7bffXl7+2c9+lpNPPrnBmB/84Ae57777cscdd+SVV17JVlttlf79++eDDz5IkkyfPj2HH354vvWtb2XChAk59dRTc+GFFzY4xsSJE9O/f/8cfvjh+cMf/pBf/OIXGTt2bM4+++zlzuuXv/xlrr322tx8882ZMmVKfvWrX2XHHXdcx2cPQFMk8gBgJQYNGpSxY8dm6tSp+ctf/pL//M//zPHHH1/ePn/+/IwePTpXXXVVDjrooPTo0SO33nprWrRokdtuuy1JMnr06HTv3j3XXntttt122xx33HHLfJ7vqquuyrHHHpvzzjsvW2+9dfbaa6/85Cc/yZ133pkFCxYsM69p06altrY2+++/f7p06ZI99tgjp5122nr9WQDQNIg8AFiJDh06ZODAgbnjjjty++23Z+DAgenQoUN5+1tvvZVPPvkkffv2La9r3rx59thjj7z++utJktdffz3f+MY3UlFRUR7Tp0+fBu8zfvz4jBkzJhtttFH51b9//yxZsiRvv/32MvM64ogj8vHHH6d79+457bTT8sADD2TRokXr+vQBaIKaNfYEAOCr7uSTTy7fNnnjjTc22FYqlZKkQcAtXb903dIxK7NkyZKcfvrpy/1c3fK+5KWuri5vvPFGHn/88TzxxBMZMmRIrrrqqjzzzDNp3rz56p0YAIXkSh4ArMKAAQOycOHCLFy4MP3792+wbauttkpVVVXGjh1bXvfJJ5/k5Zdfzvbbb58k6dGjR1588cUG+31+edddd81rr72WrbbaaplXVVXVcufVokWLHHroofnJT36Sp59+Oi+88EImTpy4Lk4ZgCbMlTwAWIXKysryrZeVlZUNtrVq1Spnnnlmvv/976ddu3bp0qVLrrzyynz00Uc55ZRTkiRnnHFGrrnmmgwdOjSnn356+dbMz/rhD3+Yb3zjGznrrLNy2mmnpVWrVnn99dfz+OOP5/rrr19mTmPGjMnixYuz5557pmXLlrnrrrvSokWLdO3adf38EABoMlzJA4DV0KZNm7Rp02a526644or80z/9UwYNGpRdd901b775Zh599NG0bds2yae3W95333156KGHsvPOO+enP/1pRo4c2eAYO+20U5555plMmTIle++9d3r16pUf//jH6dix43Lfc+ONN86tt96avn37ZqeddsqTTz6Zhx56KO3bt1+3Jw5Ak1NRWp0PCgAAANAkuJIHAABQICIPAACgQEQeAABAgYg8AACAAhF5AAAABSLyAAAACkTkAQAAFIjIAwAAKBCRBwAAUCAiDwAAoEBEHgAAQIGIPAAAgAL5vzAaW/5g1sE5AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "x = ['Croston', 'ADIDA', 'TSB']\n", + "y = [croston_errors['CFE'], adida_errors['CFE'], tsb_errors['CFE']]\n", + "\n", + "errors = [\n", + " [abs(croston_errors['CFE_min']), abs(adida_errors['CFE_min']), abs(tsb_errors['CFE_min'])],\n", + " [croston_errors['CFE_max'], adida_errors['CFE_max'], tsb_errors['CFE_max']]\n", + "]\n", + "\n", + "ax.errorbar(x, y, yerr=errors, fmt='o')\n", + "ax.set_xlabel('Models')\n", + "ax.set_ylabel('CFE')\n", + "\n", + "for i, v in enumerate(y):\n", + " plt.text(x=i+0.03, y=v, s=str(round(v,2)), va='center')\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "e144cd5c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "abs(-4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb786e17", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/29_intermittent_error_metrics_starter.ipynb b/29_intermittent_error_metrics_starter.ipynb new file mode 100644 index 0000000..e4625a8 --- /dev/null +++ b/29_intermittent_error_metrics_starter.ipynb @@ -0,0 +1,216 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "a7722505", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from statsforecast import StatsForecast\n", + "from statsforecast.models import CrostonOptimized, ADIDA, TSB\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9c10d95", + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams[\"figure.figsize\"] = (9,6)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "501318a4", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('data/intermittent_time_series.csv')\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0094b8bc", + "metadata": {}, + "outputs": [], + "source": [ + "models = [TSB(0.1, 0.1), ADIDA(), CrostonOptimized()]\n", + "\n", + "sf = StatsForecast(\n", + " df=df,\n", + " models=models,\n", + " freq='H',\n", + " n_jobs=-1\n", + ")\n", + "\n", + "cv_df = sf.cross_validation(\n", + " df=df,\n", + " h=1,\n", + " step_size=1,\n", + " n_windows=50\n", + ")\n", + "\n", + "cv_df.index = np.arange(50, 100, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b44dc8b0", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10,8))\n", + "\n", + "ax.bar(df.index, df['y'], color='lightgray')\n", + "ax.plot(cv_df.index, cv_df['TSB'], ls='--', label='TSB')\n", + "ax.plot(cv_df.index, cv_df['CrostonOptimized'], ls=':', label='Croston')\n", + "ax.plot(cv_df.index, cv_df['ADIDA'], ls='-.', label='ADIDA')\n", + "\n", + "ax.set_ylabel('Value')\n", + "ax.set_xlabel('Time steps')\n", + "ax.legend(loc='best')\n", + "plt.xlim(40, 100)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "d1459658", + "metadata": {}, + "source": [ + "## CFE, CFE_min, CFE_max, PIS, NOS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dcc24fb9", + "metadata": {}, + "outputs": [], + "source": [ + "def errors(y_true, y_pred):\n", + " \n", + " # CFE\n", + "\n", + " \n", + " # PIS\n", + "\n", + " \n", + " # NOS\n", + " \n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "312c4b2d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9dd24bdd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66468c6f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b15a2443", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d266cfb", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "x = ['Croston', 'ADIDA', 'TSB']\n", + "\n", + "# Plot errors\n", + "\n", + "\n", + "ax.set_xlabel('Models')\n", + "ax.set_ylabel('CFE')\n", + "\n", + "for i, v in enumerate(y):\n", + " plt.text(x=i+0.03, y=v, s=str(round(v,2)), va='center')\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e144cd5c", + "metadata": {}, + "outputs": [], + "source": [ + "abs(-4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb786e17", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/30_intermittent_capstone.ipynb b/30_intermittent_capstone.ipynb new file mode 100644 index 0000000..a8b38ff --- /dev/null +++ b/30_intermittent_capstone.ipynb @@ -0,0 +1,730 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "20bb6ab7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "D:\\Anaconda\\lib\\site-packages\\statsforecast\\core.py:21: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n", + " from tqdm.autonotebook import tqdm\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from statsforecast import StatsForecast\n", + "from statsforecast.models import CrostonOptimized, ADIDA, TSB\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "667b07b3", + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams[\"figure.figsize\"] = (9,6)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2d67ee62", + "metadata": {}, + "outputs": [], + "source": [ + "def errors(y_true, y_pred):\n", + " \n", + " # CFE\n", + " cfe_all = np.cumsum(y_true - y_pred)\n", + " cfe = cfe_all.iloc[-1]\n", + " \n", + " cfe_max = np.max(cfe_all)\n", + " cfe_min = np.min(cfe_all)\n", + " \n", + " # PIS\n", + " pis_all = -np.cumsum(cfe_all)\n", + " pis = pis_all.iloc[-1]\n", + " \n", + " # NOS\n", + " nos = len(cfe_all[cfe_all > 0])\n", + "\n", + " errors = {\n", + " \"CFE\": cfe,\n", + " \"CFE_max\": cfe_max,\n", + " \"CFE_min\": cfe_min,\n", + " \"PIS\": pis,\n", + " \"NOS\": nos\n", + " }\n", + " \n", + " return errors" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "a380cf6e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id parts_id date volume\n", + "0 1 2674 1998-01-01 0\n", + "1 2 2674 1998-02-01 0\n", + "2 3 2674 1998-03-01 0\n", + "3 4 2674 1998-04-01 0\n", + "4 5 2674 1998-05-01 2" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('data/car_parts_monthly_sales.csv')\n", + "\n", + "df['date'] = pd.to_datetime(df['date'])\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "3878f69a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2674, 2673, 2672, 2671, 2670, 2669, 2668], dtype=int64)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['parts_id'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "ce6df3b3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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unique_iddsy
026741998-01-010
126741998-02-010
226741998-03-010
326741998-04-010
426741998-05-012
............
35226682001-11-010
35326682001-12-010
35426682002-01-011
35526682002-02-011
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357 rows × 3 columns

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" + ], + "text/plain": [ + " unique_id ds y\n", + "0 2674 1998-01-01 0\n", + "1 2674 1998-02-01 0\n", + "2 2674 1998-03-01 0\n", + "3 2674 1998-04-01 0\n", + "4 2674 1998-05-01 2\n", + ".. ... ... ..\n", + "352 2668 2001-11-01 0\n", + "353 2668 2001-12-01 0\n", + "354 2668 2002-01-01 1\n", + "355 2668 2002-02-01 1\n", + "356 2668 2002-03-01 1\n", + "\n", + "[357 rows x 3 columns]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_df = df.drop(['id'], axis=1)\n", + "model_df.rename({\"parts_id\": \"unique_id\", \"date\": \"ds\",\n", + " \"volume\": \"y\"}, axis=1, inplace=True)\n", + "\n", + "model_df" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "bf965ec1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dscutoffyTSBADIDACrostonOptimized
unique_id
26682001-04-012001-03-012.01.2154140.7073300.781058
26682001-05-012001-03-012.01.2154140.7073300.781058
26682001-06-012001-03-016.01.2154140.7073300.781058
26682001-07-012001-03-010.01.2154140.7073300.781058
26682001-08-012001-07-011.01.5436231.8778552.074792
.....................
26742001-11-012001-07-012.01.2043510.6429590.986814
26742001-12-012001-11-012.01.0844670.8572750.751457
26742002-01-012001-11-010.01.0844670.8572750.751457
26742002-02-012001-11-011.01.0844670.8572750.751457
26742002-03-012001-11-011.01.0844670.8572750.751457
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84 rows × 6 columns

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" + ], + "text/plain": [ + " ds cutoff y TSB ADIDA CrostonOptimized\n", + "unique_id \n", + "2668 2001-04-01 2001-03-01 2.0 1.215414 0.707330 0.781058\n", + "2668 2001-05-01 2001-03-01 2.0 1.215414 0.707330 0.781058\n", + "2668 2001-06-01 2001-03-01 6.0 1.215414 0.707330 0.781058\n", + "2668 2001-07-01 2001-03-01 0.0 1.215414 0.707330 0.781058\n", + "2668 2001-08-01 2001-07-01 1.0 1.543623 1.877855 2.074792\n", + "... ... ... ... ... ... ...\n", + "2674 2001-11-01 2001-07-01 2.0 1.204351 0.642959 0.986814\n", + "2674 2001-12-01 2001-11-01 2.0 1.084467 0.857275 0.751457\n", + "2674 2002-01-01 2001-11-01 0.0 1.084467 0.857275 0.751457\n", + "2674 2002-02-01 2001-11-01 1.0 1.084467 0.857275 0.751457\n", + "2674 2002-03-01 2001-11-01 1.0 1.084467 0.857275 0.751457\n", + "\n", + "[84 rows x 6 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "models = [TSB(0.1, 0.1), ADIDA(), CrostonOptimized()]\n", + "\n", + "sf = StatsForecast(\n", + " df=model_df,\n", + " models=models,\n", + " freq='MS',\n", + " n_jobs=-1\n", + ")\n", + "\n", + "cv_df = sf.cross_validation(\n", + " df=model_df,\n", + " h=4,\n", + " step_size=4,\n", + " n_windows=3\n", + ")\n", + "\n", + "cv_df" + ] + }, + { + "cell_type": "markdown", + "id": "df17fab6", + "metadata": {}, + "source": [ + "## Evaluation " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "3f05bc59", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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UNVf0lG79lxTTzt2ZOIkPiVevuF5afmC5Pt7+sSZ1nuTulDyCRzeljDEaP3685s2bpyVLlqhRo0buTgkAUEdlnMjVh2v3l/l8u/h6jqZU5sm8cmObRYY4mlJHc/LLjY2xBTi+PpVXoDV7jqlHk3D5+3pX9hQAAABqv+AIqd3t7s6iVMOaD9PyA8s1b+c8jbtynPy8/dydktt5dFNq7Nixeu+99/T5558rJCREBw8elCTZbDYFBARcZG8AAKpOk8hg/WlgizKf75BQz/H1FWGB5cZ2bljf8XWsLaDM2LOFRRrSLsbx+Lvth/XAnHWy+nipZ5NwXdsyUte2iFRCWGAlzgQAAADucHX81YoKjNKh04e0aO8i3dD4Bnen5HYe3ZSaOXOmJKlPnz5O47Nnz9bo0aNdnxAAoO7yzZRX/UVlPv3dYft2jtf/+k6/b/d7+XjZ/7n9du+32n58uyz+PST9L8DnRJnHtVqkPK/ektpIks6cLVSszV+/ZuUqddthpW47LGmLmkQEKcYWoH/c0UERIfaPsM9ZtVcLNx0sM9+pt7VVfH17M+vjtP36bP2BMmOfHtpGjSOCJUlf/PirPlhjX6+xcUSQ/t+Q1vLxrjFLVNZay5Yt0/PPP6+0tDRlZGRo3rx5uvnmm92dFgAA7pH9q7RvpeRvk5pe5+5sHHy8fHRb89v06oZX9eG2D2lKycObUsYYd6cAAIAkaW/2Xr3646uV3u+etvfI53//3H6z7xt9uftLBfoGqkNkB0nSodOHyj3u6z++romdJmpk65G6tWO8brkyTtsPnVLqtkyl/pyptXuPa9fhHO06nKO8gkLHfr8cydHynUfKPO6Z/OLYfcdOlxt7Kq/A8fWBE2ccsct3HlH3xuG6vm1MWbvCRXJyctS+fXvdfffduu2229ydDgAA7vXzV9KCR6RmAz2qKSVJtza9Va//+LrWZa7TzuM71bR+U3en5FYe3ZQCAMBTxATF6I4Wd1R6Py9L8VVE3WK6Kcg3SM3rN3eMhfuHl3ncA6cOaPmB5Xph7Qv68fCPerrn0wr2C1aL6BC1iA7RmGuaKOvMWa3ec0yn8wsUFlS8LsHQDnFKirOVmVeUzd/x9eCkaDWJCCozNqF+8ccDr2sZqRibv1J/ztRnG37VnFV7aUp5gMGDB2vw4MEVjs/Ly1NeXp7jcXZ2dnWkBQCAe8R0kOI6S9FJ7s6khKigKF2bcK12ntipY7nH3J2O29GUAgCgAlqEtdCfu//5so5xc9ObdXPTm53G4kPiyzyuMUYfbPtAf1/zdy3au0g7ju/QP/r8w+kvarYAX/VvHVVi36Q4W7lNqfO1iglVq5jQCsU2iwpRs6gQdUqsr89//FXf7zyq3YdPOT7eh5ohOTlZTz31lLvTAABcYPPmzUpK8rxGSo2T0EX6/bfuzqJMT/V6SiG+IbJYLNVy/Jr0fcQiEAAAeCiLxaLftvyt3h70tqICo/RL9i+6a8Fd2nZsm7tTU3z9QPVtESlJem/VPjdng8qaPHmysrKyHFt6erq7UwIAoM4I9QuttoZUTcOVUgAAeLj2Ee314Y0f6tGlj8rH20fN6jdzd0qSpOHdr9C3P2dq9S/HZIwpt7g6ffa0Mk9nyuptVUxw8cf90rPTVWgKy9zvQhm5GUrIS5DNar8KLLcgVwdzDsrHy0fxIfGXfjJ1jNVqldVqdXcaAABUv4J8ycfv4nFukFuQqx9+/UF9r+jr7lTchqYUAAA1QJh/mF7v/7pyC3Md61TlFuQqOz9bkYGRbsnpmuaRevferurVpIEsFouMMdqbvVdrD61V2qE0jWw9Uq3CW0mSVmSs0ITUCeoQ0UHvXv+u4xijUkbp8JnDZb1EqR4NfFQjWo+QJP107CeNXDhSiaGJ+vKWL6vu5AAAQM22fo70zZNS84HS0FfcnU0J+YX5GvzpYB05c0Rzh8xVm/A27k7JLWhKAQBQQ3h7eSvIq3hB8mdXPaul+5dq2jXT1Dm6s8vz8bJIsRHZ+mj7Ykcj6vwGU7P6zRxNKV8vX4X4hSjI13lB9WC/YOUW5lb4NYsKi+TnVfzXTi+LV6nHBQAAdZxfkJSTKR3a4u5MSuXn7aeu0V21IXODjp456u503IamFAAANdCp/FPacnSLTuSd0Nmis47xv3z/F+04vqNSx7q+0fUa2WakJOnImSMa9+04+Xj56D/X/8cR89ya57Tu0Dqn/TJyMkrcNcbXy1dtG7RV5+jO6hHTwzHeO763frjzhxKvPf/m+ZXKdfPmzUpqWbxwZ/uI9qUet645deqUdu7c6Xi8Z88ebdiwQWFhYbriiivcmBkAAG7SqLd032IpsqW7MynTlG5TFOwbLG8vb3en4jY0pQAAqIg9y6Sv/ihFJUm3zy4e/+hu6dBm6YZp9uJHktJXS5+PlcKaSHfNLY797EFp/xppwDNS8wH2sYyN0if3SqFx0sjPimO/nCj9slzqcp/U7Q8l0gn2C9Z/rv+PVmWsUo/Y4ubP7qzd2nK0cn8R7BjV0fF1QVGBthzdIl8vX6eY9Oz0Uo/r7+2v2ICW2rs/Sl1ju2rGrUNl9WatIldbu3atrr32WsfjSZMmSZJGjRqlt99+201ZAQDgRoFh9s2DnVsjU5JS9qSoyBRVav/2ke0VFxwnSTqYc1DrDq1TPWs9har4rsrf7vtWeQV5JfZtaGuo1uGtLzHzqkNTCgCAisjPkY5sl6yhzuMn9tnH83OKx86eto95XfDPbFa6fTwvu3isIM8+VpjvHJv9q308NLZ4LOeI9NMXUssbpOBIBfgEqE9CH6fd/tT5T8rOz1ZlnCtmJKmetZ7+ed0/HetWnTOm/Rjd3uJ2p7FQv1C1CW+jNb9k6a41q7T8qLfyb/SSte7+sc9t+vTpI2OMu9MAAACXaMryKU5Xv1fEs1c966jjfjr6kx777jG1i2inyVdMLo5Z9awyT2eW2Pd3rX5HUwoAgBojoZs0eoFkDXYeHzJdyjslRbYqHotpb4/1DXCOHZgs5WZJDZoXj0W0sMf6+DvHXvdX+xVSCd2Lx37+UvpygrTu39L9qaWm2SGyQ6VP7Xz+Pv7qHd+7xHibBmUvvtmjcbiaRARp1+EcfbbhV43onnhZOQAAAFSJg5uk7f+VwhpLSbe6O5tydY3uqoKigkrt0yCggePr+v711S26mxrXa+wUc2XklTqRe6LEvomhnlGv0ZQCAKAiAsOkhr1Kjse0LzkWUL/02OikkmP+oaXHRrW2b06xNin2SqnVkOKxokLp3VukkGjp1lnF4ytfk375Tmp/Z3H86WPS/PH2K7iGvVMcu3a2tPMbe7GWdJt9LO+UNO9/Hxu8/R3J+38lw4b3pJ+/kloOkTrcKUmyWCwa3i1RT3+5VXNW7tXvul0hi8VS8pwAAABcae8KafH/Sc0He3xT6rX+r13W/h0iO+hfA/8lyb4G5zkvXPPCZR23utGUAgCgpmhzi30rOm+9gfRV0p6l9obV+TI22K+sSuhaPFaQax+7YL0oHdxkH49uWzxWdNY+Jkk672NhmVvt42HOf4X7TatAPfdfL/188KTW7TuuTomevYYDAACoAxK6SG2HSQ2vcncmKANNKQAAahqv89Z7imgp3fovqfCCBSw73GVvSMV1Lh7zt0lD/iFdsF6U2v7GfhVXTIfiMd9Ae6zkHN9qqL0hFXVeA2v3UoW+d4deibpd9+0fpP+s3EdTCgAAuF/sldJtb7g7C5SDphQAADVZYJjU7vaS4416F98N8By/IKnzPSVjE3vat/P5WEuPTehi38639XOp4IzaR1qk/dJXmzL01NA2CvX3Lbk/AAAA8D80pQAAwOW5YZrUfKAaRLfTnyJy1L91lELzMqW0T6Suvy+54DsAAIAr5WZLZ89IIVHuzgQX8Lp4CAAAQDksFqn5QFlCYzT22qZqHhUiLXteWvQX6dP7i+OO7pLmDpfmP+S8/3fT7eM7vy0ey9pvH5s3xik0fPtc+/jPC4oHTx22j300uurPDQAA1GzLX5SmJkipz7g7E5SCK6UAAEDVu6K7zM5vZOn+QPFY7gn7Ium2BOfYA2n28ab9isfyTtnHAsOdQgOO/yylL3L+uGHBGXusD1dkAQCAC9S7wv7fU4fcmwdKRVMKAABUqV2HT+mlrS1kiZqtlxLPW3/KdoV98XS/YOcdOt8tNb1OuqJH8VhIlD3Wx98p9HjDIarX/nop/ry7CgbUt8d6UdYAAIALNB8oPbrHvg4nPA7VGwAAqFKFRUbzf/xV3l4WTc7KVbTtf42l4IjSF08//wqpcwLqlxqbE9VZSkpyHrSGlH5cAAAAvyD7Bo/EmlIAAKBKNY8KUdeGYSosMpq7Zp+70wEAAICHoikFAACq3PDu9vUb5q5OV0FhkZuzAQAAddquVOmzsdLa2e7OBBfg43sAAKDKDUqKVniQnw5m5+rtH37RgNbRuiI8UJKUe7ZQG/dnlblvZIhVDRvYL7M/W1ik9ftOOJ7bnZmr03uOOR6HB/upSYR9jarCIqO0vccVW89f8fUDq+GsAABAjXR4m/Tj+8WLnkvSd9Ok7f+1LwHQ/rf2sdPHpPd/K1m8pHtSimN/eEX6ab7U4S6p02j7WH6O9O4t9q9HfSH5WO1fr/mXtPFDKek2qdsfqv3UajqaUgAAoMpZfbx1e+cEvbZ0l/721U86lJ2rJ25oLUk6cipPw15fUea+w7tdoWduaStJOplbUEps8d1zbu0Yp+nDOkiyN7CGvb5CE/o104R+zav0fAAAQA2W2EPyDZCuebR47NhuKX2V1HxQ8VjhWfuY5YIPlZ3Yax9v1Lt4zBTZxyTJmPNi0+3j8V2Ei6MpBdRCmzdvVtKFCwEDgIvdc1VDrdt7XEdy8hQWZHWM+3h5qXFE2QuONggujvW2WJxi8/Py5Gctfj4yxPnufI0jglQ/0K8q0gcAALVFTHvp7oWSxVI81uX3UvPBUkTL4jF/m3THnJL7dxwlNbpGatCseMzHvzjW27d4vP1v7Q2psEb2x4VnpT3L7HcaRgk0pQAAQLWIDPHXh2N6lBiPtvlr8R/7VOgYtkBfp9jymu7+vt4VPi4AAKhjYto5P47tYN/O5+svtRpSct/oJPt2Pm/f0mMjW9k3yd6QmtFROrFP+n2qFNfxUrOvtVjoHAAAAAAAoKp5+0oJ3aSgCClrv7uz8UhcKQUAAAAAAFAdBk2VrKGSD8sLlIamFAAAAAAAQHUIauDuDDwaH98DAAAAAACobkd2Ot+pDzSlAAAAAAAAqo0x0js3Sa90kg6sc3c2HoWmFAAAAAAAQHWxWKSQaMniLf1KU+p8rCkFAAAAAABQna59Qur/tL05BQeaUgAAAAAAANWpfqK7M/BIfHwPAAAAAADAVXKzWPD8f2hKAQAAAAAAuMKXk6QXWkj717o7E49AUwoAAAAAAMAVzp6WCs5I2xe6OxOPwJpSAAAAAAAArnDVJKnzPVJ8F3dn4hFoSgEAAAAAALhCRHN3Z+BR+PgeAAAAAACAqxUV1fkFz2lKAQAAAAAAuNKKf0ovt5fSV7s7E7eiKQUAAAAAAOBKh7ZIJ/ZJP77n7kzcijWlAAAAAAAAXKn7A1LDq6Q2t7g7E7eiKQUAAAAAAOBK0W3tWx3Hx/cAAAAAAADgcjSlAAAAAAAA3OHnr6R/D5X2rXR3Jm5BUwoAAAAAAMAdti2Qdi+R8nOKx5ZMlV5sK/0wo3gsP8c+9mJb6eyZ4vHvX7KPLX2ueMyY4tjTx4rHV71uH/vmyeo6m0qjKQUAAAAAAOAOXe+XvHykptcVj505br8z35kTxWPG2MdO7LN/7Yg98b/Y487HLS02N9s+dn6jys1Y6BwAAAAAAMAdYtpLD613HusxVmo7TAqJLh7zDZDuW2z/2sdaPN7lXqnlECk4wvkY52L9Q4vHrhwuNekrBYVXXf6XiaYUAAAAAACAu9S7ouTjC8e8vKX4TiX3tcXbt/NZLKXHhsbaNw/Cx/cAAAAAAADgcjWiKfXqq6+qUaNG8vf3V6dOnfTdd9+5OyUAAACPQr0EAABqGo9vSn3wwQeaMGGCnnjiCa1fv15XX321Bg8erH379rk7NQAAAI9AvQQAAGoij29KTZ8+Xffee6/uu+8+tWrVSi+++KISEhI0c+ZMd6cGAADgEaiXAABATeTRC53n5+crLS1Njz/+uNP4gAED9MMPP5S6T15envLy8hyPs7KyJEnZ2dnVluepU6cqdPyKxgFlqUvfa7XhHCqiLs0pUBU84Wfh3Oub82+x7EZ1sV7yhO+DsnhybnVFbfi3tTacgzt48vvBnMKVPOH7qKL1kkc3pY4cOaLCwkJFRUU5jUdFRengwYOl7pOcnKynnnqqxHhCQkK15AgAAOqmkydPymazuTsN6iUAAOCxLlYveXRT6hyLxeL02BhTYuycyZMna9KkSY7HRUVFOnbsmMLDw8vcB2XLzs5WQkKC0tPTFRoa6u504GLMf93G/NddzH35jDE6efKkYmM965bK1Evuw89M3cb8123Mf93G/JetovWSRzelGjRoIG9v7xJ/5cvMzCzx18BzrFarrFar01i9evWqK8U6IzQ0lB+yOoz5r9uY/7qLuS+bJ1whdQ71kufgZ6ZuY/7rNua/bmP+S1eResmjFzr38/NTp06dtGjRIqfxRYsWqWfPnm7KCgAAwHNQLwEAgJrKo6+UkqRJkyZpxIgR6ty5s3r06KFZs2Zp3759GjNmjLtTAwAA8AjUSwAAoCby+KbUHXfcoaNHj+rpp59WRkaGkpKStGDBAiUmJro7tTrBarXqr3/9a4lL/FE3MP91G/NfdzH3NQ/1knvxM1O3Mf91G/NftzH/l89iPOV+xgAAAAAAAKgzPHpNKQAAAAAAANRONKUAAAAAAADgcjSlAAAAAAAA4HI0pQAAAAAAAOByNKU8xLJly3TjjTcqNjZWFotFn332mdPzhw4d0ujRoxUbG6vAwEANGjRIO3bscIrZtWuXbrnlFkVERCg0NFTDhg3ToUOHnGK2b9+uoUOHqkGDBgoNDVWvXr2Umpp60fw2bdqka665RgEBAYqLi9PTTz+tC9fIz8vL0xNPPKHExERZrVY1adJEb731VrnHTU5OVpcuXRQSEqLIyEjdfPPN2rZtm1PMk08+qZYtWyooKEj169dXv379tGrVqovmXJO4av7XrVun/v37q169egoPD9f999+vU6dOXTS/i83/6NGjZbFYSmxt2rQp97gVmf9Tp05p3Lhxio+PV0BAgFq1aqWZM2deNOeaoiLvgTFGTz75pGJjYxUQEKA+ffpoy5YtTjF5eXkaP368GjRooKCgIN10003av3+/U8zx48c1YsQI2Ww22Ww2jRgxQidOnLhojhX5+Z8zZ47at2+vwMBAxcTE6O6779bRo0fLPe7MmTPVrl07hYaGKjQ0VD169NDChQudYj799FMNHDhQDRo0kMVi0YYNGy6ab03iyvl/5pln1LNnTwUGBqpevXoVyi83N1ejR49W27Zt5ePjo5tvvrnUuKVLl6pTp07y9/dX48aN9dprr1Xo+K+++qoaNWokf39/derUSd99953T86X9bunevXuFjo3aiXqJeol6iXqJeol6iXqpdtVLNKU8RE5Ojtq3b69XXnmlxHPGGN18883avXu3Pv/8c61fv16JiYnq16+fcnJyHPsPGDBAFotFixcv1vfff6/8/HzdeOONKioqchzrhhtuUEFBgRYvXqy0tDR16NBBQ4YM0cGDB8vMLTs7W/3791dsbKzWrFmjGTNm6IUXXtD06dOd4oYNG6Zvv/1Wb775prZt26b3339fLVu2LPe8ly5dqrFjx2rlypVatGiRCgoKNGDAAMd5SVLz5s31yiuvaNOmTVq+fLkaNmyoAQMG6PDhwxV6b2sCV8z/r7/+qn79+qlp06ZatWqVUlJStGXLFo0ePbrc3Coy/y+99JIyMjIcW3p6usLCwnT77beXe+yKzP/EiROVkpKi//znP/rpp580ceJEjR8/Xp9//nlF316PVpH34LnnntP06dP1yiuvaM2aNYqOjlb//v118uRJR8yECRM0b948zZ07V8uXL9epU6c0ZMgQFRYWOmLuuusubdiwQSkpKUpJSdGGDRs0YsSIcvOryPwvX75cI0eO1L333qstW7boo48+0po1a3TfffeVe+z4+HhNnTpVa9eu1dq1a9W3b18NHTrUqYDIyclRr169NHXq1Aq/pzWJK+c/Pz9ft99+ux544IEK51dYWKiAgAA99NBD6tevX6kxe/bs0fXXX6+rr75a69ev15QpU/TQQw/pk08+KffYH3zwgSZMmKAnnnhC69ev19VXX63Bgwdr3759TnGDBg1y+v2yYMGCCueP2od6iXqJeol6iXqJeol6qZbVSwYeR5KZN2+e4/G2bduMJLN582bHWEFBgQkLCzNvvPGGMcaY//73v8bLy8tkZWU5Yo4dO2YkmUWLFhljjDl8+LCRZJYtW+aIyc7ONpLMN998U2Y+r776qrHZbCY3N9cxlpycbGJjY01RUZExxpiFCxcam81mjh49elnnnpmZaSSZpUuXlhmTlZV10Zxrsuqa/9dff91ERkaawsJCR8z69euNJLNjx44y86nI/F9o3rx5xmKxmF9++aVS517a/Ldp08Y8/fTTTnEdO3Y0f/7znyt17JriwvegqKjIREdHm6lTpzpicnNzjc1mM6+99poxxpgTJ04YX19fM3fuXEfMgQMHjJeXl0lJSTHGGLN161YjyaxcudIRs2LFCiPJ/Pzzz2XmU5H5f/75503jxo2d9nv55ZdNfHx8pc+/fv365l//+leJ8T179hhJZv369ZU+Zk1SXfN/vtmzZxubzVbp3EaNGmWGDh1aYvzRRx81LVu2dBr7wx/+YLp3717u8bp27WrGjBnjNNayZUvz+OOPX/Q1AWOol6iXqJeol6iXLkS9RL1UE3GlVA2Ql5cnSfL393eMeXt7y8/PT8uXL3fEWCwWWa1WR4y/v7+8vLwcMeHh4WrVqpX+/e9/KycnRwUFBXr99dcVFRWlTp06lfn6K1as0DXXXON07IEDB+rXX3/VL7/8IkmaP3++OnfurOeee05xcXFq3ry5HnnkEZ05c6ZS55qVlSVJCgsLK/X5/Px8zZo1SzabTe3bt6/UsWuqqpr/vLw8+fn5ycur+Mc+ICBAkhwxpanI/F/ozTffVL9+/ZSYmFipcy1t/q+66irNnz9fBw4ckDFGqamp2r59uwYOHFipY9cUF74He/bs0cGDBzVgwABHjNVq1TXXXKMffvhBkpSWlqazZ886xcTGxiopKckRs2LFCtlsNnXr1s0R0717d9lsNkdMaSoy/z179tT+/fu1YMECGWN06NAhffzxx7rhhhsqfN6FhYWaO3eucnJy1KNHjwrvV9tU1/xXpxUrVji9tmT/Hlm7dq3Onj1b6j75+flKS0srsd+AAQNK5LxkyRJFRkaqefPm+v3vf6/MzMyqPQHUGtRLxaiX7KiXqJeol2on6qXaVS/RlKoBWrZsqcTERE2ePFnHjx9Xfn6+pk6dqoMHDyojI0OS/ZdlUFCQHnvsMZ0+fVo5OTn605/+pKKiIkeMxWLRokWLtH79eoWEhMjf31//+Mc/lJKSUu7nZQ8ePKioqCinsXOPz13Gvnv3bi1fvlybN2/WvHnz9OKLL+rjjz/W2LFjK3yexhhNmjRJV111lZKSkpye+/LLLxUcHOzIedGiRWrQoEGFj12TVdX89+3bVwcPHtTzzz+v/Px8HT9+XFOmTJEkR0xpKjL/58vIyNDChQsveinyhcqa/5dfflmtW7dWfHy8/Pz8NGjQIL366qu66qqrKnX8mqC09+Dce1zaHJx77uDBg/Lz81P9+vXLjYmMjCzxmpGRkeV+HKUi89+zZ0/NmTNHd9xxh/z8/BQdHa169eppxowZFz3nTZs2KTg4WFarVWPGjNG8efPUunXri+5XG1Xn/Fensr5HCgoKdOTIkVL3OXLkiAoLC8s9L0kaPHiw5syZo8WLF2vatGlas2aN+vbt6/ifT+B81EvUS9RL1EvUS7Uf9VLtq5doStUAvr6++uSTT7R9+3aFhYUpMDBQS5Ys0eDBg+Xt7S1JioiI0EcffaQvvvhCwcHBstlsysrKUseOHR0xxhg9+OCDioyM1HfffafVq1dr6NChGjJkiOMf2TZt2ig4OFjBwcEaPHiwIweLxeKUk/nfon3nxouKimSxWDRnzhx17dpV119/vaZPn663335bZ86c0Xfffec4bnBwsObMmVPiPMeNG6eNGzfq/fffL/Hctddeqw0bNuiHH37QoEGDNGzYsBrV/b0cVTX/bdq00TvvvKNp06YpMDBQ0dHRaty4saKiopxiLmX+z/f222+rXr16Tgv8Xc78v/zyy1q5cqXmz5+vtLQ0TZs2TQ8++KC++eabS3g3PVt5PwOlzUFp7395MaXFnx9zqfO/detWPfTQQ/p//+//KS0tTSkpKdqzZ4/GjBkjqfz5b9GihTZs2KCVK1fqgQce0KhRo7R169Zyz6u2qu75v5iy5r8iyvseKW/+L3Zed9xxh2644QYlJSXpxhtv1MKFC7V9+3Z99dVXlcoPdQP1EvUS9RL1EvVS7Ue9VPvqJR93J4CK6dSpkzZs2KCsrCzl5+crIiJC3bp1U+fOnR0xAwYM0K5du3TkyBH5+PioXr16io6OVqNGjSRJixcv1pdffqnjx48rNDRUkn0l/0WLFumdd97R448/rgULFjguHzx3qXJ0dHSJ7vG5Audc1zYmJkZxcXGy2WyOmFatWskYo/3796tz585Od4G4sNs7fvx4zZ8/X8uWLVN8fHyJ8w8KClLTpk3VtGlTde/eXc2aNdObb76pyZMnX9L7WdNUxfxL9oUb77rrLh06dEhBQUGyWCyaPn26I+ZS5/8cY4zeeustjRgxQn5+fo7xS53/M2fOaMqUKZo3b57j0uZ27dppw4YNeuGFF8pcSLAmKus9iI6OlmT/60pMTIxjPDMz0/E+RkdHO/6ae/5ffzIzM9WzZ09HzIV3F5Kkw4cPO45zqfOfnJysXr166U9/+pMk+xwFBQXp6quv1t/+9rdy59/Pz09NmzaVZP8+WbNmjV566SW9/vrrFXvjaonqnv+KKG3+K6Ks7xEfHx+Fh4fLZrOVmH+r1Spvb+9S97vw98P5YmJilJiYWOJuWsA51EvUS9RL1EvUS7UX9VLJ8ypNTauXuFKqhrHZbIqIiNCOHTu0du1aDR06tERMgwYNVK9ePS1evFiZmZm66aabJEmnT5+WJKfPyJ97fO6OI4mJiY5iJi4uTpLUo0cPLVu2TPn5+Y59vv76a8XGxqphw4aSpF69eunXX391ul3u9u3b5eXl5bg17bnjNm3aVCEhIZLs/yiPGzdOn376qRYvXuxUEJTHGFNjLkesSpcz/+eLiopScHCwPvjgA/n7+6t///6SLn3+z1m6dKl27type++912n8Uuf/7NmzOnv2bInvWW9vb6e7JNVkF3sPGjVqpOjoaC1atMgxlp+fr6VLlzr+Ae3UqZN8fX2dYjIyMrR582ZHTI8ePZSVlaXVq1c7YlatWqWsrCxHzKXO/+nTp0udo3PnV9b8l/V+1KWfbVfNf0WUNv8V0aNHD6fXluzfI507d5avr2+p8+/n56dOnTqV2G/RokXl5nz06FGlp6c7FZxAaaiX5NivLv1OPYd6yY56iXqptqBequX1UtWum45LdfLkSbN+/XrH3T2mT59u1q9fb/bu3WuMMebDDz80qampZteuXeazzz4ziYmJ5tZbb3U6xltvvWVWrFhhdu7cad59910TFhZmJk2a5Hj+8OHDJjw83Nx6661mw4YNZtu2beaRRx4xvr6+ZsOGDWXmduLECRMVFWXuvPNOs2nTJvPpp5+a0NBQ88ILLzjlHx8fb37zm9+YLVu2mKVLl5pmzZqZ++67r9zzfuCBB4zNZjNLliwxGRkZju306dPGGGNOnTplJk+ebFasWGF++eUXk5aWZu69915jtVqd7q5S07li/o0xZsaMGSYtLc1s27bNvPLKKyYgIMC89NJL5eZWkfk/53e/+53p1q1bhc/7YvNvjDHXXHONadOmjUlNTTW7d+82s2fPNv7+/ubVV1+t8Ot4soq8B1OnTjU2m818+umnZtOmTebOO+80MTExJjs72xEzZswYEx8fb7755huzbt0607dvX9O+fXtTUFDgiBk0aJBp166dWbFihVmxYoVp27atGTJkSLn5VWT+Z8+ebXx8fMyrr75qdu3aZZYvX246d+5sunbtWu6xJ0+ebJYtW2b27NljNm7caKZMmWK8vLzM119/7Yg5evSoWb9+vfnqq6+MJDN37lyzfv16k5GRUeH32JO5cv737t1r1q9fb5566ikTHBzs+J1z8uTJcnPcsmWLWb9+vbnxxhtNnz59HPuds3v3bhMYGGgmTpxotm7dat58803j6+trPv7443KPO3fuXOPr62vefPNNs3XrVjNhwgQTFBTkuAvVyZMnzR//+Efzww8/mD179pjU1FTTo0cPExcX53TuqFuol6iXqJeol6iXqJeol2pXvURTykOkpqYaSSW2UaNGGWOMeemll0x8fLzx9fU1V1xxhfnzn/9s8vLynI7x2GOPmaioKOPr62uaNWtmpk2bVuIWtGvWrDEDBgwwYWFhJiQkxHTv3t0sWLDgovlt3LjRXH311cZqtZro6Gjz5JNPljj2Tz/9ZPr162cCAgJMfHy8mTRpktMvitKUds6SzOzZs40xxpw5c8bccsstJjY21vj5+ZmYmBhz0003mdWrV18055rEVfM/YsQIExYWZvz8/Ey7du3Mv//97wrlV5H5P3HihAkICDCzZs2q8HlfbP6NMSYjI8OMHj3axMbGGn9/f9OiRYtSz62mqsh7UFRUZP7617+a6OhoY7VaTe/evc2mTZucjnPmzBkzbtw4ExYWZgICAsyQIUPMvn37nGKOHj1qhg8fbkJCQkxISIgZPny4OX78+EVzrMj8v/zyy6Z169YmICDAxMTEmOHDh5v9+/eXe9x77rnHJCYmGj8/PxMREWGuu+46pwLLGHsBV9r789e//vWiedcErpz/UaNGlfpaqamp5eaYmJhY6n7nW7JkibnyyiuNn5+fadiwoZk5c2aFzv+f//yn43ugY8eOTrc3P336tBkwYICJiIhw/O4bNWpUifNC3UK9RL1EvUS9RL1EvUS9VLvqJYsx/1tdCwAAAAAAAHAR1pQCAAAAAACAy9GUAgAAAAAAgMvRlAIAAAAAAIDL0ZQCAAAAAACAy9GUAgAAAAAAgMvRlAIAAAAAAIDL0ZQCAAAAAACAy9GUAgAAAAAAgMvRlAIAAAAAAIDL0ZQCUKuMHj1aFotFFotFvr6+ioqKUv/+/fXWW2+pqKiowsd5++23Va9evepLFAAAwE2olwB4CppSAGqdQYMGKSMjQ7/88osWLlyoa6+9Vg8//LCGDBmigoICd6cHAADgdtRLADwBTSkAtY7ValV0dLTi4uLUsWNHTZkyRZ9//rkWLlyot99+W5I0ffp0tW3bVkFBQUpISNCDDz6oU6dOSZKWLFmiu+++W1lZWY6/Ij755JOSpPz8fD366KOKi4tTUFCQunXrpiVLlrjnRAEAAC4R9RIAT0BTCkCd0LdvX7Vv316ffvqpJMnLy0svv/yyNm/erHfeeUeLFy/Wo48+Kknq2bOnXnzxRYWGhiojI0MZGRl65JFHJEl33323vv/+e82dO1cbN27U7bffrkGDBmnHjh1uOzcAAICqQL0EwNUsxhjj7iQAoKqMHj1aJ06c0GeffVbiud/+9rfauHGjtm7dWuK5jz76SA888ICOHDkiyb5GwoQJE3TixAlHzK5du9SsWTPt379fsbGxjvF+/fqpa9euevbZZ6v8fAAAAKoa9RIAT+Hj7gQAwFWMMbJYLJKk1NRUPfvss9q6dauys7NVUFCg3Nxc5eTkKCgoqNT9161bJ2OMmjdv7jSel5en8PDwas8fAACgulEvAXAlmlIA6oyffvpJjRo10t69e3X99ddrzJgx+r//+z+FhYVp+fLluvfee3X27Nky9y8qKpK3t7fS0tLk7e3t9FxwcHB1pw8AAFDtqJcAuBJNKQB1wuLFi7Vp0yZNnDhRa9euVUFBgaZNmyYvL/vSeh9++KFTvJ+fnwoLC53GrrzyShUWFiozM1NXX321y3IHAABwBeolAK5GUwpArZOXl6eDBw+qsLBQhw4dUkpKipKTkzVkyBCNHDlSmzZtUkFBgWbMmKEbb7xR33//vV577TWnYzRs2FCnTp3St99+q/bt2yswMFDNmzfX8OHDNXLkSE2bNk1XXnmljhw5osWLF6tt27a6/vrr3XTGAAAAlUO9BMATcPc9ALVOSkqKYmJi1LBhQw0aNEipqal6+eWX9fnnn8vb21sdOnTQ9OnT9fe//11JSUmaM2eOkpOTnY7Rs2dPjRkzRnfccYciIiL03HPPSZJmz56tkSNH6o9//KNatGihm266SatWrVJCQoI7ThUAAOCSUC8B8ATcfQ8AAAAAAAAux5VSAAAAAAAAcDmaUgAAAAAAAHA5mlIAAAAAAABwOZpSAAAAAAAAcDmaUgAAAAAAAHA5mlIAAAAAAABwOZpSAAAAAAAAcDmaUgAAAAAAAHA5mlIAAAAAAABwOZpSAAAAAAAAcDmaUgAAAAAAAHC5/w+0lI5VTAZbogAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12,9))\n", + "\n", + "for i, ax in enumerate(axes.flatten()):\n", + " \n", + " ax.bar(\n", + " x=df[df['parts_id'] == parts_ids[i]]['date'],\n", + " height=df[df['parts_id'] == parts_ids[i]]['volume'],\n", + " color='lightgray'\n", + " )\n", + " ax.plot(\n", + " cv_df[cv_df.index==parts_ids[i]]['ds'],\n", + " cv_df[cv_df.index==parts_ids[i]]['CrostonOptimized'],\n", + " ls='--',\n", + " label='Croston'\n", + " )\n", + " \n", + " ax.plot(\n", + " cv_df[cv_df.index==parts_ids[i]]['ds'],\n", + " cv_df[cv_df.index==parts_ids[i]]['ADIDA'],\n", + " ls=':',\n", + " label='ADIDA'\n", + " )\n", + " \n", + " ax.plot(\n", + " cv_df[cv_df.index==parts_ids[i]]['ds'],\n", + " cv_df[cv_df.index==parts_ids[i]]['TSB'],\n", + " ls='-.',\n", + " label='TSB'\n", + " )\n", + "\n", + " ax.set_xlabel('Date')\n", + " ax.set_ylabel('Volume')\n", + " ax.legend(loc='best')\n", + " \n", + " ax.xaxis.set_major_locator(plt.MaxNLocator(5))\n", + " \n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "71159b55", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'CFE': -41.35299, 'CFE_max': 8.651390075683594, 'CFE_min': -44.09335708618164, 'PIS': 2060.1028, 'NOS': 12}\n", + "{'CFE': -5.0277653, 'CFE_max': 9.537116050720215, 'CFE_min': -12.577536582946777, 'PIS': 269.7729, 'NOS': 26}\n", + "{'CFE': -34.143932, 'CFE_max': 8.507474899291992, 'CFE_min': -34.601707458496094, 'PIS': 1423.5746, 'NOS': 14}\n" + ] + } + ], + "source": [ + "croston_errors = errors(cv_df['y'], cv_df['CrostonOptimized'])\n", + "adida_errors = errors(cv_df['y'], cv_df['ADIDA'])\n", + "tsb_errors = errors(cv_df['y'], cv_df['TSB'])\n", + "\n", + "print(croston_errors)\n", + "print(adida_errors)\n", + "print(tsb_errors)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "5dd24fe9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "models = ['Croston', 'ADIDA', 'TSB']\n", + "errors = {\n", + " 'CFE': (-41, -5, -34),\n", + " 'PIS': (2060, 270, 1424),\n", + " 'NOS': (12, 26, 14)\n", + "}\n", + "\n", + "x = np.arange(len(models))\n", + "width = 0.25\n", + "multiplier = 0\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "for attr, value in errors.items():\n", + " offset = width*multiplier\n", + " rects = ax.bar(x+offset, value, width, label=attr)\n", + " ax.bar_label(rects, padding=3)\n", + " multiplier += 1\n", + " \n", + "ax.set_xlabel('Models')\n", + "ax.set_ylabel('Error metrics')\n", + "ax.set_xticks(x+width, models)\n", + "ax.legend(loc='best')\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9645e9dc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/30_intermittent_capstone_starter.ipynb b/30_intermittent_capstone_starter.ipynb new file mode 100644 index 0000000..f5a075b --- /dev/null +++ b/30_intermittent_capstone_starter.ipynb @@ -0,0 +1,287 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "20bb6ab7", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from statsforecast import StatsForecast\n", + "from statsforecast.models import CrostonOptimized, ADIDA, TSB\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "667b07b3", + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams[\"figure.figsize\"] = (9,6)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d67ee62", + "metadata": {}, + "outputs": [], + "source": [ + "def errors(y_true, y_pred):\n", + " \n", + " # CFE\n", + " cfe_all = np.cumsum(y_true - y_pred)\n", + " cfe = cfe_all.iloc[-1]\n", + " \n", + " cfe_max = np.max(cfe_all)\n", + " cfe_min = np.min(cfe_all)\n", + " \n", + " # PIS\n", + " pis_all = -np.cumsum(cfe_all)\n", + " pis = pis_all.iloc[-1]\n", + " \n", + " # NOS\n", + " nos = len(cfe_all[cfe_all > 0])\n", + "\n", + " errors = {\n", + " \"CFE\": cfe,\n", + " \"CFE_max\": cfe_max,\n", + " \"CFE_min\": cfe_min,\n", + " \"PIS\": pis,\n", + " \"NOS\": nos\n", + " }\n", + " \n", + " return errors" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a380cf6e", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('data/car_parts_monthly_sales.csv')\n", + "\n", + "df['date'] = pd.to_datetime(df['date'])\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3878f69a", + "metadata": {}, + "outputs": [], + "source": [ + "# list unique parts_id\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce6df3b3", + "metadata": {}, + "outputs": [], + "source": [ + "parts_ids = df['parts_id'].unique()\n", + "\n", + "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12,9))\n", + "\n", + "for i, ax in enumerate(axes.flatten()):\n", + " \n", + " ax.bar(\n", + " x=df[df['parts_id'] == parts_ids[i]]['date'],\n", + " height=df[df['parts_id'] == parts_ids[i]]['volume'],\n", + " color='lightgray'\n", + " )\n", + "\n", + " ax.set_xlabel('Date')\n", + " ax.set_ylabel('Volume')\n", + " \n", + " ax.xaxis.set_major_locator(plt.MaxNLocator(5))\n", + " \n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "0ad928ae", + "metadata": {}, + "source": [ + "## Forecasting " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dfe545fc", + "metadata": {}, + "outputs": [], + "source": [ + "# Format dataset:\n", + "# Drop the id column (not the parts_id column)\n", + "# Rename columns to \"unique_id\", \"ds\", \"y\"\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf965ec1", + "metadata": {}, + "outputs": [], + "source": [ + "# Use a horizon of 4 months\n", + "# Use a step size of 4\n", + "# Use 3 windows\n", + "# Model with Croston, ADIDA and TSB\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "df17fab6", + "metadata": {}, + "source": [ + "## Evaluation " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f05bc59", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12,9))\n", + "\n", + "for i, ax in enumerate(axes.flatten()):\n", + " \n", + " ax.bar(\n", + " x=df[df['parts_id'] == parts_ids[i]]['date'],\n", + " height=df[df['parts_id'] == parts_ids[i]]['volume'],\n", + " color='lightgray'\n", + " )\n", + " ax.plot(\n", + " cv_df[cv_df.index==parts_ids[i]]['ds'],\n", + " cv_df[cv_df.index==parts_ids[i]]['CrostonOptimized'],\n", + " ls='--',\n", + " label='Croston'\n", + " )\n", + " \n", + " ax.plot(\n", + " cv_df[cv_df.index==parts_ids[i]]['ds'],\n", + " cv_df[cv_df.index==parts_ids[i]]['ADIDA'],\n", + " ls=':',\n", + " label='ADIDA'\n", + " )\n", + " \n", + " ax.plot(\n", + " cv_df[cv_df.index==parts_ids[i]]['ds'],\n", + " cv_df[cv_df.index==parts_ids[i]]['TSB'],\n", + " ls='-.',\n", + " label='TSB'\n", + " )\n", + "\n", + " ax.set_xlabel('Date')\n", + " ax.set_ylabel('Volume')\n", + " ax.legend(loc='best')\n", + " \n", + " ax.xaxis.set_major_locator(plt.MaxNLocator(5))\n", + " \n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71159b55", + "metadata": {}, + "outputs": [], + "source": [ + "croston_errors = errors(cv_df['y'], cv_df['CrostonOptimized'])\n", + "adida_errors = errors(cv_df['y'], cv_df['ADIDA'])\n", + "tsb_errors = errors(cv_df['y'], cv_df['TSB'])\n", + "\n", + "print(croston_errors)\n", + "print(adida_errors)\n", + "print(tsb_errors)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5dd24fe9", + "metadata": {}, + "outputs": [], + "source": [ + "models = ['Croston', 'ADIDA', 'TSB']\n", + "errors = {\n", + " 'CFE': (-41, -5, -34),\n", + " 'PIS': (2060, 270, 1424),\n", + " 'NOS': (12, 26, 14)\n", + "}\n", + "\n", + "x = np.arange(len(models))\n", + "width = 0.25\n", + "multiplier = 0\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "for attr, value in errors.items():\n", + " offset = width*multiplier\n", + " rects = ax.bar(x+offset, value, width, label=attr)\n", + " ax.bar_label(rects, padding=3)\n", + " multiplier += 1\n", + " \n", + "ax.set_xlabel('Models')\n", + "ax.set_ylabel('Error metrics')\n", + "ax.set_xticks(x+width, models)\n", + "ax.legend(loc='best')\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9645e9dc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}