Test2 Arima (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omkarshanbhag/anaconda/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Geothermal</th>\n",
       "      <th>Biomass</th>\n",
       "      <th>Biogas</th>\n",
       "      <th>Small Hydro</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>632</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-04-20 00:00:00</td>\n",
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       "      <td>2010-04-20 01:00:00</td>\n",
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       "      <th>2</th>\n",
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       "      <td>3</td>\n",
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       "      <td>2010-04-20 02:00:00</td>\n",
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       "      <td>2010-04-20 03:00:00</td>\n",
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       "      <td>2010-04-20 05:00:00</td>\n",
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       "      <td>1066</td>\n",
       "      <td>312</td>\n",
       "      <td>166</td>\n",
       "      <td>244</td>\n",
       "      <td>763</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-04-20 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1067</td>\n",
       "      <td>313</td>\n",
       "      <td>167</td>\n",
       "      <td>269</td>\n",
       "      <td>544</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2010-04-20 07:00:00</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>169</td>\n",
       "      <td>280</td>\n",
       "      <td>572</td>\n",
       "      <td>191.0</td>\n",
       "      <td>2010-04-20 08:00:00</td>\n",
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       "      <td>316</td>\n",
       "      <td>167</td>\n",
       "      <td>294</td>\n",
       "      <td>721</td>\n",
       "      <td>299.0</td>\n",
       "      <td>2010-04-20 09:00:00</td>\n",
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       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
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       "      <td>314</td>\n",
       "      <td>169</td>\n",
       "      <td>294</td>\n",
       "      <td>658</td>\n",
       "      <td>278.0</td>\n",
       "      <td>2010-04-20 10:00:00</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
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       "      <td>1066</td>\n",
       "      <td>317</td>\n",
       "      <td>171</td>\n",
       "      <td>271</td>\n",
       "      <td>772</td>\n",
       "      <td>198.0</td>\n",
       "      <td>2010-04-20 11:00:00</td>\n",
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       "      <th>12</th>\n",
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       "      <th>13</th>\n",
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       "      <td>14</td>\n",
       "      <td>1066</td>\n",
       "      <td>322</td>\n",
       "      <td>169</td>\n",
       "      <td>282</td>\n",
       "      <td>1350</td>\n",
       "      <td>139.0</td>\n",
       "      <td>2010-04-20 13:00:00</td>\n",
       "    </tr>\n",
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       "      <th>14</th>\n",
       "      <td>14</td>\n",
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       "      <td>1067</td>\n",
       "      <td>326</td>\n",
       "      <td>170</td>\n",
       "      <td>292</td>\n",
       "      <td>1489</td>\n",
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       "      <td>2010-04-20 14:00:00</td>\n",
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       "      <td>1070</td>\n",
       "      <td>324</td>\n",
       "      <td>160</td>\n",
       "      <td>294</td>\n",
       "      <td>1475</td>\n",
       "      <td>39.0</td>\n",
       "      <td>2010-04-20 15:00:00</td>\n",
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       "      <th>16</th>\n",
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       "      <td>1071</td>\n",
       "      <td>320</td>\n",
       "      <td>147</td>\n",
       "      <td>295</td>\n",
       "      <td>1518</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2010-04-20 16:00:00</td>\n",
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       "      <th>17</th>\n",
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       "      <td>320</td>\n",
       "      <td>148</td>\n",
       "      <td>297</td>\n",
       "      <td>1511</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2010-04-20 17:00:00</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
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       "      <th>19</th>\n",
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       "      <td>299</td>\n",
       "      <td>151</td>\n",
       "      <td>305</td>\n",
       "      <td>1083</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-04-20 19:00:00</td>\n",
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       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>1072</td>\n",
       "      <td>300</td>\n",
       "      <td>124</td>\n",
       "      <td>307</td>\n",
       "      <td>1126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-04-20 20:00:00</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>1071</td>\n",
       "      <td>277</td>\n",
       "      <td>123</td>\n",
       "      <td>297</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-04-20 21:00:00</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>1071</td>\n",
       "      <td>284</td>\n",
       "      <td>126</td>\n",
       "      <td>266</td>\n",
       "      <td>1017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-04-20 22:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>1072</td>\n",
       "      <td>284</td>\n",
       "      <td>140</td>\n",
       "      <td>245</td>\n",
       "      <td>1018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-04-20 23:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1072</td>\n",
       "      <td>285</td>\n",
       "      <td>145</td>\n",
       "      <td>253</td>\n",
       "      <td>1055</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-04-21 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1072</td>\n",
       "      <td>280</td>\n",
       "      <td>142</td>\n",
       "      <td>253</td>\n",
       "      <td>938</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-04-21 01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>1072</td>\n",
       "      <td>277</td>\n",
       "      <td>153</td>\n",
       "      <td>254</td>\n",
       "      <td>864</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-04-21 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>1073</td>\n",
       "      <td>275</td>\n",
       "      <td>167</td>\n",
       "      <td>255</td>\n",
       "      <td>828</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-04-21 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>1074</td>\n",
       "      <td>282</td>\n",
       "      <td>164</td>\n",
       "      <td>263</td>\n",
       "      <td>843</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-04-21 04:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>1074</td>\n",
       "      <td>286</td>\n",
       "      <td>163</td>\n",
       "      <td>274</td>\n",
       "      <td>850</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-04-21 05:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>21082</th>\n",
       "      <td>21082</td>\n",
       "      <td>16</td>\n",
       "      <td>3374</td>\n",
       "      <td>2240</td>\n",
       "      <td>13205</td>\n",
       "      <td>6755</td>\n",
       "      <td>2201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-14 10:00:00</td>\n",
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       "    <tr>\n",
       "      <th>21083</th>\n",
       "      <td>21083</td>\n",
       "      <td>17</td>\n",
       "      <td>2987</td>\n",
       "      <td>2239</td>\n",
       "      <td>14676</td>\n",
       "      <td>6561</td>\n",
       "      <td>2453</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-14 11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21084</th>\n",
       "      <td>21084</td>\n",
       "      <td>18</td>\n",
       "      <td>2928</td>\n",
       "      <td>2239</td>\n",
       "      <td>16505</td>\n",
       "      <td>6758</td>\n",
       "      <td>2622</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-14 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21085</th>\n",
       "      <td>21085</td>\n",
       "      <td>19</td>\n",
       "      <td>2925</td>\n",
       "      <td>2240</td>\n",
       "      <td>16105</td>\n",
       "      <td>7221</td>\n",
       "      <td>2572</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-14 13:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21086</th>\n",
       "      <td>21086</td>\n",
       "      <td>20</td>\n",
       "      <td>2917</td>\n",
       "      <td>2240</td>\n",
       "      <td>15448</td>\n",
       "      <td>7423</td>\n",
       "      <td>2542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-14 14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21087</th>\n",
       "      <td>21087</td>\n",
       "      <td>21</td>\n",
       "      <td>3155</td>\n",
       "      <td>2240</td>\n",
       "      <td>14137</td>\n",
       "      <td>7683</td>\n",
       "      <td>2404</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-14 15:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21088</th>\n",
       "      <td>21088</td>\n",
       "      <td>22</td>\n",
       "      <td>3055</td>\n",
       "      <td>2242</td>\n",
       "      <td>13259</td>\n",
       "      <td>7265</td>\n",
       "      <td>2061</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-14 16:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21089</th>\n",
       "      <td>21089</td>\n",
       "      <td>23</td>\n",
       "      <td>2951</td>\n",
       "      <td>2243</td>\n",
       "      <td>11561</td>\n",
       "      <td>7007</td>\n",
       "      <td>1868</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-14 17:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21090</th>\n",
       "      <td>21090</td>\n",
       "      <td>24</td>\n",
       "      <td>3079</td>\n",
       "      <td>2242</td>\n",
       "      <td>10166</td>\n",
       "      <td>5904</td>\n",
       "      <td>1879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-14 18:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21091</th>\n",
       "      <td>21091</td>\n",
       "      <td>1</td>\n",
       "      <td>916</td>\n",
       "      <td>351</td>\n",
       "      <td>195</td>\n",
       "      <td>270</td>\n",
       "      <td>1561</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-09-14 19:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21092</th>\n",
       "      <td>21092</td>\n",
       "      <td>2</td>\n",
       "      <td>915</td>\n",
       "      <td>355</td>\n",
       "      <td>200</td>\n",
       "      <td>233</td>\n",
       "      <td>1662</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-09-14 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21093</th>\n",
       "      <td>21093</td>\n",
       "      <td>3</td>\n",
       "      <td>915</td>\n",
       "      <td>360</td>\n",
       "      <td>197</td>\n",
       "      <td>228</td>\n",
       "      <td>1770</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-09-14 21:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21094</th>\n",
       "      <td>21094</td>\n",
       "      <td>4</td>\n",
       "      <td>917</td>\n",
       "      <td>350</td>\n",
       "      <td>198</td>\n",
       "      <td>224</td>\n",
       "      <td>1794</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-09-14 22:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21095</th>\n",
       "      <td>21095</td>\n",
       "      <td>5</td>\n",
       "      <td>918</td>\n",
       "      <td>340</td>\n",
       "      <td>201</td>\n",
       "      <td>231</td>\n",
       "      <td>1526</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-09-14 23:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21096</th>\n",
       "      <td>21096</td>\n",
       "      <td>6</td>\n",
       "      <td>918</td>\n",
       "      <td>340</td>\n",
       "      <td>203</td>\n",
       "      <td>279</td>\n",
       "      <td>1034</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-09-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21097</th>\n",
       "      <td>21097</td>\n",
       "      <td>7</td>\n",
       "      <td>918</td>\n",
       "      <td>338</td>\n",
       "      <td>202</td>\n",
       "      <td>283</td>\n",
       "      <td>896</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2012-09-15 01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21098</th>\n",
       "      <td>21098</td>\n",
       "      <td>8</td>\n",
       "      <td>917</td>\n",
       "      <td>337</td>\n",
       "      <td>198</td>\n",
       "      <td>287</td>\n",
       "      <td>807</td>\n",
       "      <td>124.0</td>\n",
       "      <td>2012-09-15 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21099</th>\n",
       "      <td>21099</td>\n",
       "      <td>9</td>\n",
       "      <td>914</td>\n",
       "      <td>328</td>\n",
       "      <td>194</td>\n",
       "      <td>290</td>\n",
       "      <td>838</td>\n",
       "      <td>282.0</td>\n",
       "      <td>2012-09-15 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21100</th>\n",
       "      <td>21100</td>\n",
       "      <td>12</td>\n",
       "      <td>3734</td>\n",
       "      <td>2244</td>\n",
       "      <td>13444</td>\n",
       "      <td>7743</td>\n",
       "      <td>1916</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-15 04:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21101</th>\n",
       "      <td>21101</td>\n",
       "      <td>13</td>\n",
       "      <td>3801</td>\n",
       "      <td>2242</td>\n",
       "      <td>12950</td>\n",
       "      <td>7943</td>\n",
       "      <td>1877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-15 05:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21102</th>\n",
       "      <td>21102</td>\n",
       "      <td>14</td>\n",
       "      <td>3903</td>\n",
       "      <td>2241</td>\n",
       "      <td>12785</td>\n",
       "      <td>7606</td>\n",
       "      <td>1878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-15 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21103</th>\n",
       "      <td>21103</td>\n",
       "      <td>15</td>\n",
       "      <td>3566</td>\n",
       "      <td>2240</td>\n",
       "      <td>13951</td>\n",
       "      <td>6210</td>\n",
       "      <td>1926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-15 07:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21104</th>\n",
       "      <td>21104</td>\n",
       "      <td>16</td>\n",
       "      <td>3238</td>\n",
       "      <td>2237</td>\n",
       "      <td>13618</td>\n",
       "      <td>6741</td>\n",
       "      <td>1951</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-15 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21105</th>\n",
       "      <td>21105</td>\n",
       "      <td>17</td>\n",
       "      <td>2962</td>\n",
       "      <td>2236</td>\n",
       "      <td>14851</td>\n",
       "      <td>6611</td>\n",
       "      <td>2034</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-15 09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21106</th>\n",
       "      <td>21106</td>\n",
       "      <td>18</td>\n",
       "      <td>2690</td>\n",
       "      <td>2237</td>\n",
       "      <td>16175</td>\n",
       "      <td>7281</td>\n",
       "      <td>2262</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-15 10:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21107</th>\n",
       "      <td>21107</td>\n",
       "      <td>19</td>\n",
       "      <td>2586</td>\n",
       "      <td>2238</td>\n",
       "      <td>16316</td>\n",
       "      <td>6807</td>\n",
       "      <td>2412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-15 11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21108</th>\n",
       "      <td>21108</td>\n",
       "      <td>20</td>\n",
       "      <td>2642</td>\n",
       "      <td>2239</td>\n",
       "      <td>15716</td>\n",
       "      <td>6664</td>\n",
       "      <td>2286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-15 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21109</th>\n",
       "      <td>21109</td>\n",
       "      <td>21</td>\n",
       "      <td>2896</td>\n",
       "      <td>2240</td>\n",
       "      <td>13684</td>\n",
       "      <td>7498</td>\n",
       "      <td>2302</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-15 13:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21110</th>\n",
       "      <td>21110</td>\n",
       "      <td>22</td>\n",
       "      <td>2862</td>\n",
       "      <td>2241</td>\n",
       "      <td>12663</td>\n",
       "      <td>7195</td>\n",
       "      <td>2296</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-15 14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21111</th>\n",
       "      <td>21111</td>\n",
       "      <td>23</td>\n",
       "      <td>2991</td>\n",
       "      <td>2242</td>\n",
       "      <td>11170</td>\n",
       "      <td>6655</td>\n",
       "      <td>2329</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-15 15:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21112 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  Hour  Geothermal  Biomass  Biogas  Small Hydro  Wind  \\\n",
       "0               0     1        1056      323     170          250   632   \n",
       "1               1     2        1055      318     169          245   632   \n",
       "2               2     3        1058      316     168          240   677   \n",
       "3               3     4        1063      318     168          238   923   \n",
       "4               4     5        1066      318     168          247   875   \n",
       "5               5     6        1066      317     169          247   998   \n",
       "6               6     7        1066      312     166          244   763   \n",
       "7               7     8        1067      313     167          269   544   \n",
       "8               8     9        1065      315     169          280   572   \n",
       "9               9    10        1063      316     167          294   721   \n",
       "10             10    11        1063      314     169          294   658   \n",
       "11             11    12        1066      317     171          271   772   \n",
       "12             12    13        1068      324     171          270  1012   \n",
       "13             13    14        1066      322     169          282  1350   \n",
       "14             14    15        1067      326     170          292  1489   \n",
       "15             15    16        1070      324     160          294  1475   \n",
       "16             16    17        1071      320     147          295  1518   \n",
       "17             17    18        1070      320     148          297  1511   \n",
       "18             18    19        1070      314     153          302  1356   \n",
       "19             19    20        1072      299     151          305  1083   \n",
       "20             20    21        1072      300     124          307  1126   \n",
       "21             21    22        1071      277     123          297  1024   \n",
       "22             22    23        1071      284     126          266  1017   \n",
       "23             23    24        1072      284     140          245  1018   \n",
       "24             24     1        1072      285     145          253  1055   \n",
       "25             25     2        1072      280     142          253   938   \n",
       "26             26     3        1072      277     153          254   864   \n",
       "27             27     4        1073      275     167          255   828   \n",
       "28             28     5        1074      282     164          263   843   \n",
       "29             29     6        1074      286     163          274   850   \n",
       "...           ...   ...         ...      ...     ...          ...   ...   \n",
       "21082       21082    16        3374     2240   13205         6755  2201   \n",
       "21083       21083    17        2987     2239   14676         6561  2453   \n",
       "21084       21084    18        2928     2239   16505         6758  2622   \n",
       "21085       21085    19        2925     2240   16105         7221  2572   \n",
       "21086       21086    20        2917     2240   15448         7423  2542   \n",
       "21087       21087    21        3155     2240   14137         7683  2404   \n",
       "21088       21088    22        3055     2242   13259         7265  2061   \n",
       "21089       21089    23        2951     2243   11561         7007  1868   \n",
       "21090       21090    24        3079     2242   10166         5904  1879   \n",
       "21091       21091     1         916      351     195          270  1561   \n",
       "21092       21092     2         915      355     200          233  1662   \n",
       "21093       21093     3         915      360     197          228  1770   \n",
       "21094       21094     4         917      350     198          224  1794   \n",
       "21095       21095     5         918      340     201          231  1526   \n",
       "21096       21096     6         918      340     203          279  1034   \n",
       "21097       21097     7         918      338     202          283   896   \n",
       "21098       21098     8         917      337     198          287   807   \n",
       "21099       21099     9         914      328     194          290   838   \n",
       "21100       21100    12        3734     2244   13444         7743  1916   \n",
       "21101       21101    13        3801     2242   12950         7943  1877   \n",
       "21102       21102    14        3903     2241   12785         7606  1878   \n",
       "21103       21103    15        3566     2240   13951         6210  1926   \n",
       "21104       21104    16        3238     2237   13618         6741  1951   \n",
       "21105       21105    17        2962     2236   14851         6611  2034   \n",
       "21106       21106    18        2690     2237   16175         7281  2262   \n",
       "21107       21107    19        2586     2238   16316         6807  2412   \n",
       "21108       21108    20        2642     2239   15716         6664  2286   \n",
       "21109       21109    21        2896     2240   13684         7498  2302   \n",
       "21110       21110    22        2862     2241   12663         7195  2296   \n",
       "21111       21111    23        2991     2242   11170         6655  2329   \n",
       "\n",
       "       Solar                 Date  \n",
       "0        0.0  2010-04-20 00:00:00  \n",
       "1        0.0  2010-04-20 01:00:00  \n",
       "2        0.0  2010-04-20 02:00:00  \n",
       "3        0.0  2010-04-20 03:00:00  \n",
       "4        0.0  2010-04-20 04:00:00  \n",
       "5        0.0  2010-04-20 05:00:00  \n",
       "6        0.0  2010-04-20 06:00:00  \n",
       "7        1.0  2010-04-20 07:00:00  \n",
       "8      191.0  2010-04-20 08:00:00  \n",
       "9      299.0  2010-04-20 09:00:00  \n",
       "10     278.0  2010-04-20 10:00:00  \n",
       "11     198.0  2010-04-20 11:00:00  \n",
       "12     126.0  2010-04-20 12:00:00  \n",
       "13     139.0  2010-04-20 13:00:00  \n",
       "14     111.0  2010-04-20 14:00:00  \n",
       "15      39.0  2010-04-20 15:00:00  \n",
       "16      25.0  2010-04-20 16:00:00  \n",
       "17       3.0  2010-04-20 17:00:00  \n",
       "18       0.0  2010-04-20 18:00:00  \n",
       "19       0.0  2010-04-20 19:00:00  \n",
       "20       0.0  2010-04-20 20:00:00  \n",
       "21       0.0  2010-04-20 21:00:00  \n",
       "22       0.0  2010-04-20 22:00:00  \n",
       "23       0.0  2010-04-20 23:00:00  \n",
       "24       0.0  2010-04-21 00:00:00  \n",
       "25       0.0  2010-04-21 01:00:00  \n",
       "26       0.0  2010-04-21 02:00:00  \n",
       "27       0.0  2010-04-21 03:00:00  \n",
       "28       0.0  2010-04-21 04:00:00  \n",
       "29       0.0  2010-04-21 05:00:00  \n",
       "...      ...                  ...  \n",
       "21082    NaN  2012-09-14 10:00:00  \n",
       "21083    NaN  2012-09-14 11:00:00  \n",
       "21084    NaN  2012-09-14 12:00:00  \n",
       "21085    NaN  2012-09-14 13:00:00  \n",
       "21086    NaN  2012-09-14 14:00:00  \n",
       "21087    NaN  2012-09-14 15:00:00  \n",
       "21088    NaN  2012-09-14 16:00:00  \n",
       "21089    NaN  2012-09-14 17:00:00  \n",
       "21090    NaN  2012-09-14 18:00:00  \n",
       "21091    0.0  2012-09-14 19:00:00  \n",
       "21092    0.0  2012-09-14 20:00:00  \n",
       "21093    0.0  2012-09-14 21:00:00  \n",
       "21094    0.0  2012-09-14 22:00:00  \n",
       "21095    0.0  2012-09-14 23:00:00  \n",
       "21096    0.0  2012-09-15 00:00:00  \n",
       "21097    6.0  2012-09-15 01:00:00  \n",
       "21098  124.0  2012-09-15 02:00:00  \n",
       "21099  282.0  2012-09-15 03:00:00  \n",
       "21100    NaN  2012-09-15 04:00:00  \n",
       "21101    NaN  2012-09-15 05:00:00  \n",
       "21102    NaN  2012-09-15 06:00:00  \n",
       "21103    NaN  2012-09-15 07:00:00  \n",
       "21104    NaN  2012-09-15 08:00:00  \n",
       "21105    NaN  2012-09-15 09:00:00  \n",
       "21106    NaN  2012-09-15 10:00:00  \n",
       "21107    NaN  2012-09-15 11:00:00  \n",
       "21108    NaN  2012-09-15 12:00:00  \n",
       "21109    NaN  2012-09-15 13:00:00  \n",
       "21110    NaN  2012-09-15 14:00:00  \n",
       "21111    NaN  2012-09-15 15:00:00  \n",
       "\n",
       "[21112 rows x 9 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_data_complete = pd.read_csv(\"renewables_data.cleaned.csv\")\n",
    "ts_data_complete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Geothermal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-04-20 00:00:00</th>\n",
       "      <td>1056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 01:00:00</th>\n",
       "      <td>1055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 02:00:00</th>\n",
       "      <td>1058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 03:00:00</th>\n",
       "      <td>1063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 04:00:00</th>\n",
       "      <td>1066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 05:00:00</th>\n",
       "      <td>1066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 06:00:00</th>\n",
       "      <td>1066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 07:00:00</th>\n",
       "      <td>1067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 08:00:00</th>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 09:00:00</th>\n",
       "      <td>1063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 10:00:00</th>\n",
       "      <td>1063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 11:00:00</th>\n",
       "      <td>1066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 12:00:00</th>\n",
       "      <td>1068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 13:00:00</th>\n",
       "      <td>1066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 14:00:00</th>\n",
       "      <td>1067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 15:00:00</th>\n",
       "      <td>1070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 16:00:00</th>\n",
       "      <td>1071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 17:00:00</th>\n",
       "      <td>1070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 18:00:00</th>\n",
       "      <td>1070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 19:00:00</th>\n",
       "      <td>1072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 20:00:00</th>\n",
       "      <td>1072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 21:00:00</th>\n",
       "      <td>1071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 22:00:00</th>\n",
       "      <td>1071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-20 23:00:00</th>\n",
       "      <td>1072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-21 00:00:00</th>\n",
       "      <td>1072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-21 01:00:00</th>\n",
       "      <td>1072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-21 02:00:00</th>\n",
       "      <td>1072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-21 03:00:00</th>\n",
       "      <td>1073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-21 04:00:00</th>\n",
       "      <td>1074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-21 05:00:00</th>\n",
       "      <td>1074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-14 10:00:00</th>\n",
       "      <td>3374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-14 11:00:00</th>\n",
       "      <td>2987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-14 12:00:00</th>\n",
       "      <td>2928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-14 13:00:00</th>\n",
       "      <td>2925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-14 14:00:00</th>\n",
       "      <td>2917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-14 15:00:00</th>\n",
       "      <td>3155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-14 16:00:00</th>\n",
       "      <td>3055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-14 17:00:00</th>\n",
       "      <td>2951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-14 18:00:00</th>\n",
       "      <td>3079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-14 19:00:00</th>\n",
       "      <td>916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-14 20:00:00</th>\n",
       "      <td>915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-14 21:00:00</th>\n",
       "      <td>915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-14 22:00:00</th>\n",
       "      <td>917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-14 23:00:00</th>\n",
       "      <td>918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-15 00:00:00</th>\n",
       "      <td>918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-15 01:00:00</th>\n",
       "      <td>918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-15 02:00:00</th>\n",
       "      <td>917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-15 03:00:00</th>\n",
       "      <td>914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-15 04:00:00</th>\n",
       "      <td>3734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-15 05:00:00</th>\n",
       "      <td>3801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-15 06:00:00</th>\n",
       "      <td>3903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-15 07:00:00</th>\n",
       "      <td>3566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-15 08:00:00</th>\n",
       "      <td>3238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-15 09:00:00</th>\n",
       "      <td>2962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-15 10:00:00</th>\n",
       "      <td>2690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-15 11:00:00</th>\n",
       "      <td>2586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-15 12:00:00</th>\n",
       "      <td>2642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-15 13:00:00</th>\n",
       "      <td>2896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-15 14:00:00</th>\n",
       "      <td>2862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-15 15:00:00</th>\n",
       "      <td>2991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21112 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Geothermal\n",
       "Date                           \n",
       "2010-04-20 00:00:00        1056\n",
       "2010-04-20 01:00:00        1055\n",
       "2010-04-20 02:00:00        1058\n",
       "2010-04-20 03:00:00        1063\n",
       "2010-04-20 04:00:00        1066\n",
       "2010-04-20 05:00:00        1066\n",
       "2010-04-20 06:00:00        1066\n",
       "2010-04-20 07:00:00        1067\n",
       "2010-04-20 08:00:00        1065\n",
       "2010-04-20 09:00:00        1063\n",
       "2010-04-20 10:00:00        1063\n",
       "2010-04-20 11:00:00        1066\n",
       "2010-04-20 12:00:00        1068\n",
       "2010-04-20 13:00:00        1066\n",
       "2010-04-20 14:00:00        1067\n",
       "2010-04-20 15:00:00        1070\n",
       "2010-04-20 16:00:00        1071\n",
       "2010-04-20 17:00:00        1070\n",
       "2010-04-20 18:00:00        1070\n",
       "2010-04-20 19:00:00        1072\n",
       "2010-04-20 20:00:00        1072\n",
       "2010-04-20 21:00:00        1071\n",
       "2010-04-20 22:00:00        1071\n",
       "2010-04-20 23:00:00        1072\n",
       "2010-04-21 00:00:00        1072\n",
       "2010-04-21 01:00:00        1072\n",
       "2010-04-21 02:00:00        1072\n",
       "2010-04-21 03:00:00        1073\n",
       "2010-04-21 04:00:00        1074\n",
       "2010-04-21 05:00:00        1074\n",
       "...                         ...\n",
       "2012-09-14 10:00:00        3374\n",
       "2012-09-14 11:00:00        2987\n",
       "2012-09-14 12:00:00        2928\n",
       "2012-09-14 13:00:00        2925\n",
       "2012-09-14 14:00:00        2917\n",
       "2012-09-14 15:00:00        3155\n",
       "2012-09-14 16:00:00        3055\n",
       "2012-09-14 17:00:00        2951\n",
       "2012-09-14 18:00:00        3079\n",
       "2012-09-14 19:00:00         916\n",
       "2012-09-14 20:00:00         915\n",
       "2012-09-14 21:00:00         915\n",
       "2012-09-14 22:00:00         917\n",
       "2012-09-14 23:00:00         918\n",
       "2012-09-15 00:00:00         918\n",
       "2012-09-15 01:00:00         918\n",
       "2012-09-15 02:00:00         917\n",
       "2012-09-15 03:00:00         914\n",
       "2012-09-15 04:00:00        3734\n",
       "2012-09-15 05:00:00        3801\n",
       "2012-09-15 06:00:00        3903\n",
       "2012-09-15 07:00:00        3566\n",
       "2012-09-15 08:00:00        3238\n",
       "2012-09-15 09:00:00        2962\n",
       "2012-09-15 10:00:00        2690\n",
       "2012-09-15 11:00:00        2586\n",
       "2012-09-15 12:00:00        2642\n",
       "2012-09-15 13:00:00        2896\n",
       "2012-09-15 14:00:00        2862\n",
       "2012-09-15 15:00:00        2991\n",
       "\n",
       "[21112 rows x 1 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_series_geo = ts_data_complete[['Date', 'Geothermal']]\n",
    "time_series_geo = time_series_geo.set_index('Date')\n",
    "time_series_geo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_series_geo = time_series_geo.astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "yjIvPJ2x0ckg",
    "outputId": "d3cd65ca-c847-4d56-9a75-d8cc8174610e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             ARIMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:           D.Geothermal   No. Observations:                21111\n",
      "Model:                 ARIMA(2, 1, 2)   Log Likelihood             -163606.586\n",
      "Method:                       css-mle   S.D. of innovations            561.590\n",
      "Date:                Fri, 27 Apr 2018   AIC                         327225.173\n",
      "Time:                        00:39:11   BIC                         327272.918\n",
      "Sample:                    04-20-2010   HQIC                        327240.753\n",
      "                         - 09-15-2012                                         \n",
      "======================================================================================\n",
      "                         coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "const                  0.0883      3.874      0.023      0.982      -7.504       7.681\n",
      "ar.L1.D.Geothermal     0.8305      0.000   3978.872      0.000       0.830       0.831\n",
      "ar.L2.D.Geothermal    -0.9997      0.000  -5303.702      0.000      -1.000      -0.999\n",
      "ma.L1.D.Geothermal    -0.8239      0.003   -323.839      0.000      -0.829      -0.819\n",
      "ma.L2.D.Geothermal     0.9958      0.001   1883.259      0.000       0.995       0.997\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                 Real           Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1            0.4153           -0.9098j            1.0001           -0.1818\n",
      "AR.2            0.4153           +0.9098j            1.0001            0.1818\n",
      "MA.1            0.4137           -0.9127j            1.0021           -0.1823\n",
      "MA.2            0.4137           +0.9127j            1.0021            0.1823\n",
      "-----------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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wpYj0Tq9UiqIoSjrIeqUGDAYKjDGrjTEHgFeAEaEiGwPb9hxg864yfvvfb/ii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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118993ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vZgfDGnRjNOk4uWJlxtYLTI7L1Fo5It2q2WnK3yQ4gz8dLj8J/Oscu60DDtc9HgnXNbNN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\nSChgREQkEgoYERGJxP8HNoFlRj5X7ukAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117e69278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  0\n",
      "count  21111.000000\n",
      "mean      -0.002420\n",
      "std      561.603296\n",
      "min    -3729.244443\n",
      "25%     -125.955267\n",
      "50%        0.014756\n",
      "75%      120.996730\n",
      "max     4115.869846\n"
     ]
    }
   ],
   "source": [
    "from pandas import read_csv\n",
    "from pandas import datetime\n",
    "from pandas import DataFrame\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from matplotlib import pyplot\n",
    "\n",
    "#def parser(x):\n",
    "#\treturn datetime.strptime('20'+x, '%Y-%m' '%h:%m')\n",
    "\n",
    "#series = read_csv('TS1.csv', header=0, parse_dates=[0], index_col=0, squeeze=True)\n",
    "series = time_series_geo\n",
    "#series[8729:]\n",
    "# fit model\n",
    "model = ARIMA(series, order=(2,1,2))\n",
    "model_fit = model.fit(disp=0)\n",
    "print(model_fit.summary())\n",
    "# plot residual errors\n",
    "residuals = DataFrame(model_fit.resid)\n",
    "residuals.plot()\n",
    "pyplot.show()\n",
    "residuals.plot(kind='kde')\n",
    "pyplot.show()\n",
    "print(residuals.describe())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "5qlSe9LS0ckz",
    "outputId": "c05f1593-10fe-40ac-d9f6-b4808127ceae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted=1918.417901, expected=2197.000000\n",
      "predicted=2206.619649, expected=2455.000000\n",
      "predicted=2468.802604, expected=2459.000000\n",
      "predicted=2466.880953, expected=2416.000000\n",
      "predicted=2416.315274, expected=2473.000000\n",
      "predicted=2464.761946, expected=2443.000000\n",
      "predicted=2433.969798, expected=2372.000000\n",
      "predicted=2370.136946, expected=2689.000000\n",
      "predicted=2697.348753, expected=993.000000\n",
      "predicted=950.042774, expected=994.000000\n",
      "predicted=959.788574, expected=994.000000\n",
      "predicted=970.894152, expected=994.000000\n",
      "predicted=984.640511, expected=992.000000\n",
      "predicted=1050.966129, expected=992.000000\n",
      "predicted=991.913551, expected=991.000000\n",
      "predicted=990.937744, expected=991.000000\n",
      "predicted=990.973840, expected=990.000000\n",
      "predicted=990.022124, expected=2006.000000\n",
      "predicted=2034.435030, expected=1924.000000\n",
      "predicted=1945.782178, expected=1978.000000\n",
      "predicted=1992.770390, expected=2194.000000\n",
      "predicted=2199.190166, expected=2254.000000\n",
      "predicted=2226.411366, expected=2301.000000\n",
      "predicted=2309.843281, expected=2489.000000\n",
      "predicted=2494.269865, expected=2502.000000\n",
      "predicted=2500.041124, expected=2451.000000\n",
      "predicted=2450.661699, expected=2396.000000\n",
      "predicted=2391.730630, expected=2624.000000\n",
      "predicted=2621.864321, expected=2645.000000\n",
      "predicted=2649.855388, expected=2787.000000\n",
      "predicted=2796.817122, expected=993.000000\n",
      "predicted=948.396273, expected=994.000000\n",
      "predicted=945.815740, expected=994.000000\n",
      "predicted=966.523204, expected=994.000000\n",
      "predicted=990.737136, expected=993.000000\n",
      "predicted=1055.272128, expected=993.000000\n",
      "predicted=992.937333, expected=994.000000\n",
      "predicted=994.008694, expected=994.000000\n",
      "predicted=994.020221, expected=994.000000\n",
      "predicted=994.045229, expected=3323.000000\n",
      "predicted=3387.968325, expected=3248.000000\n",
      "predicted=3301.000666, expected=3061.000000\n",
      "predicted=3088.752654, expected=2920.000000\n",
      "predicted=2908.586434, expected=2911.000000\n",
      "predicted=2824.083382, expected=2861.000000\n",
      "predicted=2860.221995, expected=2870.000000\n",
      "predicted=2875.683732, expected=2883.000000\n",
      "predicted=2887.859179, expected=2889.000000\n",
      "predicted=2890.089009, expected=2867.000000\n",
      "predicted=2868.572332, expected=2678.000000\n",
      "predicted=2672.114697, expected=2615.000000\n",
      "predicted=2608.147537, expected=2693.000000\n",
      "predicted=2690.831285, expected=994.000000\n",
      "predicted=948.731819, expected=995.000000\n",
      "predicted=962.910100, expected=994.000000\n",
      "predicted=970.934273, expected=994.000000\n",
      "predicted=992.833323, expected=993.000000\n",
      "predicted=1051.866036, expected=992.000000\n",
      "predicted=991.910675, expected=991.000000\n",
      "predicted=990.964183, expected=991.000000\n",
      "predicted=990.958148, expected=991.000000\n",
      "predicted=991.016253, expected=2066.000000\n",
      "predicted=2095.881767, expected=1959.000000\n",
      "predicted=1981.355804, expected=1914.000000\n",
      "predicted=1926.221769, expected=1918.000000\n",
      "predicted=1914.550640, expected=1869.000000\n",
      "predicted=1829.982677, expected=1803.000000\n",
      "predicted=1803.886511, expected=1778.000000\n",
      "predicted=1776.641798, expected=1807.000000\n",
      "predicted=1806.197885, expected=1783.000000\n",
      "predicted=1784.455280, expected=1813.000000\n",
      "predicted=1816.059358, expected=1799.000000\n",
      "predicted=1799.851618, expected=1786.000000\n",
      "predicted=1784.824157, expected=1790.000000\n",
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      "/Users/omkarshanbhag/anaconda/lib/python3.6/site-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
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      "/Users/omkarshanbhag/anaconda/lib/python3.6/site-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
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      "/Users/omkarshanbhag/anaconda/lib/python3.6/site-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
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      "/Users/omkarshanbhag/anaconda/lib/python3.6/site-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
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      "/Users/omkarshanbhag/anaconda/lib/python3.6/site-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omkarshanbhag/anaconda/lib/python3.6/site-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted=2449.964867, expected=2318.000000\n",
      "predicted=2305.189786, expected=2399.000000\n",
      "predicted=2397.481503, expected=2389.000000\n",
      "predicted=2392.796364, expected=2343.000000\n",
      "predicted=2351.228158, expected=2380.000000\n",
      "predicted=2382.458388, expected=2231.000000\n",
      "predicted=2225.858092, expected=2162.000000\n",
      "predicted=2157.902468, expected=927.000000\n",
      "predicted=890.862108, expected=928.000000\n",
      "predicted=902.664824, expected=928.000000\n",
      "predicted=917.496582, expected=927.000000\n",
      "predicted=931.140757, expected=927.000000\n",
      "predicted=947.897419, expected=927.000000\n",
      "predicted=926.966430, expected=925.000000\n",
      "predicted=924.941563, expected=925.000000\n",
      "predicted=924.971484, expected=924.000000\n",
      "predicted=923.945123, expected=2593.000000\n",
      "predicted=2638.813634, expected=2588.000000\n",
      "predicted=2620.860994, expected=2479.000000\n",
      "predicted=2493.831618, expected=2362.000000\n",
      "predicted=2352.628759, expected=2106.000000\n",
      "predicted=2067.307767, expected=2029.000000\n",
      "predicted=2021.000688, expected=2366.000000\n",
      "predicted=2373.191136, expected=2316.000000\n",
      "predicted=2323.116002, expected=1982.000000\n",
      "predicted=1980.026062, expected=1768.000000\n",
      "predicted=1755.536570, expected=1691.000000\n",
      "predicted=1675.567597, expected=1683.000000\n",
      "predicted=1680.687066, expected=1701.000000\n",
      "predicted=1706.729800, expected=925.000000\n",
      "predicted=907.788921, expected=926.000000\n",
      "predicted=912.274508, expected=926.000000\n",
      "predicted=917.830357, expected=925.000000\n",
      "predicted=926.575166, expected=924.000000\n",
      "predicted=937.056500, expected=924.000000\n",
      "predicted=923.946712, expected=922.000000\n",
      "predicted=921.930793, expected=920.000000\n",
      "predicted=919.918933, expected=918.000000\n",
      "predicted=917.895065, expected=2668.000000\n",
      "predicted=2715.999326, expected=2727.000000\n",
      "predicted=2763.121253, expected=2732.000000\n",
      "predicted=2752.070996, expected=2576.000000\n",
      "predicted=2568.264044, expected=2336.000000\n",
      "predicted=2296.752817, expected=2106.000000\n",
      "predicted=2092.351783, expected=2291.000000\n",
      "predicted=2289.369001, expected=2822.000000\n",
      "predicted=2841.094063, expected=2839.000000\n",
      "predicted=2856.579806, expected=2852.000000\n",
      "predicted=2861.830259, expected=2602.000000\n",
      "predicted=2591.174461, expected=2463.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omkarshanbhag/anaconda/lib/python3.6/site-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted=2445.444993, expected=2751.000000\n",
      "predicted=2753.287638, expected=925.000000\n",
      "predicted=879.622704, expected=927.000000\n",
      "predicted=898.789484, expected=927.000000\n",
      "predicted=909.217702, expected=926.000000\n",
      "predicted=925.751365, expected=926.000000\n",
      "predicted=956.856168, expected=925.000000\n",
      "predicted=924.922177, expected=925.000000\n",
      "predicted=924.976866, expected=926.000000\n",
      "predicted=926.027651, expected=922.000000\n",
      "predicted=921.906628, expected=4455.000000\n",
      "predicted=4551.737879, expected=4560.000000\n",
      "predicted=4632.435583, expected=4493.000000\n",
      "predicted=4530.971654, expected=4339.000000\n",
      "predicted=4325.843973, expected=4041.000000\n",
      "predicted=3969.269167, expected=3545.000000\n",
      "predicted=3522.447975, expected=3578.000000\n",
      "predicted=3567.710119, expected=3627.000000\n",
      "predicted=3627.305298, expected=3532.000000\n",
      "predicted=3537.244865, expected=3641.000000\n",
      "predicted=3651.156157, expected=3721.000000\n",
      "predicted=3723.749589, expected=3866.000000\n",
      "predicted=3872.233749, expected=3739.000000\n",
      "predicted=3740.666449, expected=928.000000\n",
      "predicted=847.854450, expected=929.000000\n",
      "predicted=870.994841, expected=930.000000\n",
      "predicted=898.320926, expected=930.000000\n",
      "predicted=939.828956, expected=930.000000\n",
      "predicted=977.992492, expected=929.000000\n",
      "predicted=928.947004, expected=928.000000\n",
      "predicted=927.930154, expected=928.000000\n",
      "predicted=927.964093, expected=927.000000\n",
      "predicted=926.958880, expected=2706.000000\n",
      "predicted=2754.818468, expected=2639.000000\n",
      "predicted=2671.969221, expected=2589.000000\n",
      "predicted=2605.103277, expected=2475.000000\n",
      "predicted=2465.459423, expected=2141.000000\n",
      "predicted=2099.011569, expected=1838.000000\n",
      "predicted=1823.310981, expected=1881.000000\n",
      "predicted=1873.975726, expected=1953.000000\n",
      "predicted=1955.557590, expected=1919.000000\n",
      "predicted=1926.524782, expected=1906.000000\n",
      "predicted=1910.840776, expected=1823.000000\n",
      "predicted=1819.215387, expected=1771.000000\n",
      "predicted=1766.716326, expected=1762.000000\n",
      "predicted=1760.519410, expected=929.000000\n",
      "predicted=905.910253, expected=929.000000\n",
      "predicted=914.214233, expected=929.000000\n",
      "predicted=921.166110, expected=929.000000\n",
      "predicted=931.470006, expected=929.000000\n",
      "predicted=943.244790, expected=929.000000\n",
      "predicted=928.994175, expected=928.000000\n",
      "predicted=927.966742, expected=927.000000\n",
      "predicted=926.947190, expected=924.000000\n",
      "predicted=923.881783, expected=2716.000000\n",
      "predicted=2765.087117, expected=2684.000000\n",
      "predicted=2718.143554, expected=2627.000000\n",
      "predicted=2643.721706, expected=2446.000000\n",
      "predicted=2434.724971, expected=2105.000000\n",
      "predicted=2061.032760, expected=1777.000000\n",
      "predicted=1760.220570, expected=1755.000000\n",
      "predicted=1745.967039, expected=1806.000000\n",
      "predicted=1807.660334, expected=1807.000000\n",
      "predicted=1814.622134, expected=1784.000000\n",
      "predicted=1789.651186, expected=1724.000000\n",
      "predicted=1722.180771, expected=1691.000000\n",
      "predicted=1687.836641, expected=1687.000000\n",
      "predicted=1685.697725, expected=925.000000\n",
      "predicted=904.264063, expected=926.000000\n",
      "predicted=912.246529, expected=926.000000\n",
      "predicted=918.624167, expected=926.000000\n",
      "predicted=928.254305, expected=927.000000\n",
      "predicted=940.101578, expected=926.000000\n",
      "predicted=925.968981, expected=925.000000\n",
      "predicted=924.957565, expected=924.000000\n",
      "predicted=923.933746, expected=923.000000\n",
      "predicted=922.922258, expected=2598.000000\n",
      "predicted=2643.940270, expected=2566.000000\n",
      "predicted=2597.861303, expected=2489.000000\n",
      "predicted=2503.868048, expected=2317.000000\n",
      "predicted=2305.741806, expected=2018.000000\n",
      "predicted=1977.047221, expected=1734.000000\n",
      "predicted=1719.409816, expected=1724.000000\n",
      "predicted=1716.937334, expected=1737.000000\n",
      "predicted=1738.070651, expected=1762.000000\n",
      "predicted=1768.846698, expected=1790.000000\n",
      "predicted=1796.343632, expected=1794.000000\n",
      "predicted=1795.083495, expected=1823.000000\n",
      "predicted=1823.902080, expected=1831.000000\n",
      "predicted=1831.348802, expected=925.000000\n",
      "predicted=900.140047, expected=926.000000\n",
      "predicted=908.302004, expected=925.000000\n",
      "predicted=915.025412, expected=925.000000\n",
      "predicted=927.502221, expected=925.000000\n",
      "predicted=940.586843, expected=925.000000\n",
      "predicted=924.979709, expected=924.000000\n",
      "predicted=923.983790, expected=924.000000\n",
      "predicted=923.974479, expected=922.000000\n",
      "predicted=921.928671, expected=2653.000000\n",
      "predicted=2700.447576, expected=2635.000000\n",
      "predicted=2668.292907, expected=2577.000000\n",
      "predicted=2593.168422, expected=2551.000000\n",
      "predicted=2544.004211, expected=2381.000000\n",
      "predicted=2345.539653, expected=2047.000000\n",
      "predicted=2034.803979, expected=2132.000000\n",
      "predicted=2127.187709, expected=2376.000000\n",
      "predicted=2381.873292, expected=2382.000000\n",
      "predicted=2391.785699, expected=2158.000000\n",
      "predicted=2160.095384, expected=2010.000000\n",
      "predicted=1999.507783, expected=1836.000000\n",
      "predicted=1821.828064, expected=1758.000000\n",
      "predicted=1751.533304, expected=921.000000\n",
      "predicted=899.038620, expected=922.000000\n",
      "predicted=907.987942, expected=921.000000\n",
      "predicted=915.506932, expected=922.000000\n",
      "predicted=925.821494, expected=921.000000\n",
      "predicted=935.444104, expected=921.000000\n",
      "predicted=920.970427, expected=919.000000\n",
      "predicted=918.942876, expected=917.000000\n",
      "predicted=916.885578, expected=910.000000\n",
      "predicted=909.759087, expected=3142.000000\n",
      "predicted=3203.088012, expected=3394.000000\n",
      "predicted=3444.425921, expected=3262.000000\n",
      "predicted=3286.665804, expected=2899.000000\n",
      "predicted=2882.721088, expected=2560.000000\n",
      "predicted=2503.016873, expected=2785.000000\n",
      "predicted=2777.021506, expected=2850.000000\n",
      "predicted=2856.113497, expected=2963.000000\n",
      "predicted=2977.031152, expected=2896.000000\n",
      "predicted=2902.247806, expected=2859.000000\n",
      "predicted=2853.863317, expected=2778.000000\n",
      "predicted=2772.989075, expected=2642.000000\n",
      "predicted=2634.647397, expected=2528.000000\n",
      "predicted=2522.731556, expected=924.000000\n",
      "predicted=877.163984, expected=924.000000\n",
      "predicted=893.481861, expected=923.000000\n",
      "predicted=909.153998, expected=923.000000\n",
      "predicted=929.788084, expected=923.000000\n",
      "predicted=950.539596, expected=923.000000\n",
      "predicted=922.996947, expected=921.000000\n",
      "predicted=920.956039, expected=921.000000\n",
      "predicted=920.955013, expected=918.000000\n",
      "predicted=917.890686, expected=3623.000000\n",
      "predicted=3697.403401, expected=3704.000000\n",
      "predicted=3758.852215, expected=3712.000000\n",
      "predicted=3741.726693, expected=3663.000000\n",
      "predicted=3654.628920, expected=3374.000000\n",
      "predicted=3318.595866, expected=2987.000000\n",
      "predicted=2968.911802, expected=2928.000000\n",
      "predicted=2916.008031, expected=2925.000000\n",
      "predicted=2921.642937, expected=2917.000000\n",
      "predicted=2922.410867, expected=3155.000000\n",
      "predicted=3168.336980, expected=3055.000000\n",
      "predicted=3057.906018, expected=2951.000000\n",
      "predicted=2948.803655, expected=3079.000000\n",
      "predicted=3078.960789, expected=916.000000\n",
      "predicted=854.220546, expected=915.000000\n",
      "predicted=876.325661, expected=915.000000\n",
      "predicted=894.110391, expected=917.000000\n",
      "predicted=921.775506, expected=918.000000\n",
      "predicted=955.464149, expected=918.000000\n",
      "predicted=918.050953, expected=918.000000\n",
      "predicted=917.997553, expected=917.000000\n",
      "predicted=916.928439, expected=914.000000\n",
      "predicted=913.874496, expected=3734.000000\n",
      "predicted=3811.436538, expected=3801.000000\n",
      "predicted=3857.686159, expected=3903.000000\n",
      "predicted=3936.245078, expected=3566.000000\n",
      "predicted=3550.534630, expected=3238.000000\n",
      "predicted=3174.647664, expected=2962.000000\n",
      "predicted=2943.213854, expected=2690.000000\n",
      "predicted=2673.259715, expected=2586.000000\n",
      "predicted=2582.062336, expected=2642.000000\n",
      "predicted=2645.442434, expected=2896.000000\n",
      "predicted=2908.800803, expected=2862.000000\n",
      "predicted=2871.728844, expected=2991.000000\n",
      "Test MSE: 517298.670\n"
     ]
    },
    {
     "data": {
      "image/png": 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EWQ27dWjULI6ixtXmdtedhQ5bth0X+t7zOmBECZip0lul77j7Xul9ahn63Xaz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1198f1e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "from pandas import read_csv\n",
    "from pandas import datetime\n",
    "from matplotlib import pyplot\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "#def parser(x):\n",
    "#\treturn datetime.strptime('190'+x, '%Y-%m')\n",
    "\n",
    "series = time_series_geo\n",
    "X = series.values\n",
    "size = int(len(X) * 0.66)\n",
    "train, test = X[0:size], X[size:len(X)]\n",
    "history = [x for x in train]\n",
    "predictions = list()\n",
    "for t in range(len(test)):\n",
    "\tmodel = ARIMA(history, order=(5,1,0))\n",
    "\tmodel_fit = model.fit(disp=0)\n",
    "\toutput = model_fit.forecast()\n",
    "\tyhat = output[0]\n",
    "\tpredictions.append(yhat)\n",
    "\tobs = test[t]\n",
    "\thistory.append(obs)\n",
    "\tprint('predicted=%f, expected=%f' % (yhat, obs))\n",
    "error = mean_squared_error(test, predictions)\n",
    "print('Test MSE: %.3f' % error)\n",
    "# plot\n",
    "pyplot.plot(test)\n",
    "pyplot.plot(predictions, color='red')\n",
    "pyplot.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "wk7Rd_1l0ck_"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1918.4179008386814"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.7288438 ,  3.88328199, -1.78748108, -4.09133521,  0.53279649,\n",
       "       -0.18259454, -0.01518138,  0.12859111,  0.15312192,  0.08035008,\n",
       "        0.09215412,  0.08792331,  0.08471936,  0.08448563,  0.08559875,\n",
       "        0.08540137,  0.08549942,  0.085566  ,  0.08556808,  0.08555175,\n",
       "        0.08555512,  0.08555299])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast = model_fit.predict(start=len(time_series_geo), end=len(time_series_geo)+20)\n",
    "forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        2870.3500431 ,  2870.43039318,  2870.5225473 ,  2870.6104706 ,\n",
       "        2870.69518997,  2870.7796756 ,  2870.86527435,  2870.95067571,\n",
       "        2871.03617514,  2871.12174114,  2871.20730922,  2871.29286097,\n",
       "        2871.37841608,  2871.46396907,  2871.54952073,  2871.63507237,\n",
       "        2871.72062423,  2871.80617604,  2871.89172789,  2871.97727976,\n",
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       "        2873.08945409,  2873.17500596,  2873.26055783,  2873.3461097 ,\n",
       "        2873.43166157,  2873.51721345,  2873.60276532,  2873.68831719,\n",
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       "        2874.45828403,  2874.5438359 ,  2874.62938777,  2874.71493964,\n",
       "        2874.80049151,  2874.88604338,  2874.97159526,  2875.05714713,\n",
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       "        2877.53815139,  2877.62370326,  2877.70925513,  2877.794807  ,\n",
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       "        2878.90698133,  2878.9925332 ,  2879.07808507,  2879.16363694,\n",
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       "        2879.5913963 ,  2879.67694817,  2879.76250004,  2879.84805191,\n",
       "        2879.93360378,  2880.01915565,  2880.10470752,  2880.1902594 ,\n",
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       "        2880.61801875,  2880.70357062,  2880.78912249,  2880.87467436,\n",
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       "        2881.6446412 ,  2881.73019308,  2881.81574495,  2881.90129682,\n",
       "        2881.98684869,  2882.07240056,  2882.15795243,  2882.2435043 ,\n",
       "        2882.32905617,  2882.41460805,  2882.50015992,  2882.58571179,\n",
       "        2882.67126366,  2882.75681553,  2882.8423674 ,  2882.92791927,\n",
       "        2883.01347114,  2883.09902301,  2883.18457489,  2883.27012676,\n",
       "        2883.35567863,  2883.4412305 ,  2883.52678237,  2883.61233424,\n",
       "        2883.69788611,  2883.78343798,  2883.86898985,  2883.95454173,\n",
       "        2884.0400936 ,  2884.12564547,  2884.21119734,  2884.29674921,\n",
       "        2884.38230108,  2884.46785295,  2884.55340482,  2884.6389567 ,\n",
       "        2884.72450857,  2884.81006044,  2884.89561231,  2884.98116418,\n",
       "        2885.06671605,  2885.15226792,  2885.23781979,  2885.32337166,\n",
       "        2885.40892354,  2885.49447541,  2885.58002728,  2885.66557915,\n",
       "        2885.75113102,  2885.83668289,  2885.92223476,  2886.00778663,\n",
       "        2886.0933385 ,  2886.17889038,  2886.26444225,  2886.34999412,\n",
       "        2886.43554599,  2886.52109786,  2886.60664973,  2886.6922016 ,\n",
       "        2886.77775347,  2886.86330535,  2886.94885722,  2887.03440909,\n",
       "        2887.11996096,  2887.20551283,  2887.2910647 ,  2887.37661657,\n",
       "        2887.46216844,  2887.54772031,  2887.63327219,  2887.71882406,\n",
       "        2887.80437593,  2887.8899278 ,  2887.97547967,  2888.06103154,\n",
       "        2888.14658341,  2888.23213528,  2888.31768715,  2888.40323903,\n",
       "        2888.4887909 ,  2888.57434277,  2888.65989464,  2888.74544651,\n",
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       "        2938.10887616,  2938.19442804,  2938.27997991,  2938.36553178,\n",
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       "        2939.81991359,  2939.90546546,  2939.99101733,  2940.0765692 ,\n",
       "        2940.16212107,  2940.24767294,  2940.33322481,  2940.41877669,\n",
       "        2940.50432856,  2940.58988043,  2940.6754323 ,  2940.76098417,\n",
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       "        2944.26861089,  2944.35416276,  2944.43971463,  2944.5252665 ,\n",
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       "        2944.95302586,  2945.03857773,  2945.1241296 ,  2945.20968147,\n",
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       "        2949.05951567,  2949.14506754,  2949.23061941,  2949.31617129,\n",
       "        2949.40172316,  2949.48727503,  2949.5728269 ,  2949.65837877,\n",
       "        2949.74393064,  2949.82948251,  2949.91503438,  2950.00058625,\n",
       "        2950.08613813,  2950.17169   ,  2950.25724187,  2950.34279374,\n",
       "        2950.42834561,  2950.51389748,  2950.59944935,  2950.68500122,\n",
       "        2950.77055309,  2950.85610497,  2950.94165684,  2951.02720871,\n",
       "        2951.11276058,  2951.19831245,  2951.28386432,  2951.36941619,\n",
       "        2951.45496806,  2951.54051994,  2951.62607181,  2951.71162368,\n",
       "        2951.79717555,  2951.88272742,  2951.96827929,  2952.05383116,\n",
       "        2952.13938303,  2952.2249349 ,  2952.31048678,  2952.39603865,\n",
       "        2952.48159052,  2952.56714239,  2952.65269426,  2952.73824613,\n",
       "        2952.823798  ,  2952.90934987,  2952.99490174,  2953.08045362,\n",
       "        2953.16600549,  2953.25155736,  2953.33710923,  2953.4226611 ,\n",
       "        2953.50821297,  2953.59376484,  2953.67931671,  2953.76486859,\n",
       "        2953.85042046,  2953.93597233,  2954.0215242 ,  2954.10707607,\n",
       "        2954.19262794,  2954.27817981,  2954.36373168,  2954.44928355,\n",
       "        2954.53483543,  2954.6203873 ,  2954.70593917,  2954.79149104,\n",
       "        2954.87704291,  2954.96259478,  2955.04814665,  2955.13369852])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast = model_fit.forecast(steps=1000)[0]\n",
    "forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "default_view": {},
   "name": "Test2 Arima.ipynb",
   "provenance": [],
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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