time-series-forecasting-karachi-temp-gm.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":8731688,"sourceType":"datasetVersion","datasetId":5135614}],"dockerImageVersionId":30732,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"source":"<a href=\"https://www.kaggle.com/code/faiqueali/time-series-forecasting-karachi-temp-gm?scriptVersionId=184902136\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","source":"# Imports","metadata":{}},{"cell_type":"code","source":"!pip install tqdm -q","metadata":{"execution":{"iopub.status.busy":"2024-06-22T13:46:11.790709Z","iopub.execute_input":"2024-06-22T13:46:11.791678Z","iopub.status.idle":"2024-06-22T13:46:29.750679Z","shell.execute_reply.started":"2024-06-22T13:46:11.791637Z","shell.execute_reply":"2024-06-22T13:46:29.748655Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"import time\nimport warnings\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom datetime import datetime\nfrom tqdm.notebook import tqdm_notebook\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error\n\nwarnings.filterwarnings('ignore')","metadata":{"execution":{"iopub.status.busy":"2024-06-22T13:46:29.753451Z","iopub.execute_input":"2024-06-22T13:46:29.753928Z","iopub.status.idle":"2024-06-22T13:46:31.27839Z","shell.execute_reply.started":"2024-06-22T13:46:29.753887Z","shell.execute_reply":"2024-06-22T13:46:31.277075Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":"# Constant","metadata":{}},{"cell_type":"code","source":"DATASET_PATH = \"/kaggle/input/weather-underground/weather_data.csv\"","metadata":{"execution":{"iopub.status.busy":"2024-06-22T13:46:31.280221Z","iopub.execute_input":"2024-06-22T13:46:31.281289Z","iopub.status.idle":"2024-06-22T13:46:31.289052Z","shell.execute_reply.started":"2024-06-22T13:46:31.281233Z","shell.execute_reply":"2024-06-22T13:46:31.287409Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"# Utils","metadata":{}},{"cell_type":"code","source":"# GM(1,1) model implementation\ndef gm11(x0):\n    n = len(x0)\n    x1 = np.cumsum(x0)  # Accumulated series\n    z1 = (x1[:-1] + x1[1:]) / 2.0  # Mean sequence\n    B = np.vstack([-z1, np.ones(n-1)]).T\n    Y = x0[1:]\n    u = np.linalg.lstsq(B, Y, rcond=None)[0]\n    a, b = u\n    f = lambda k: (x0[0] - b/a) * np.exp(-a * k) + b/a\n    x0_hat = np.array([f(k) for k in range(n)])\n    return x0_hat, a, b\n\n\n# GM(1,N) model implementation\ndef gm1n(x0, N):\n    n = len(x0)\n    x1 = np.cumsum(x0)  # Accumulated series\n    z1 = np.zeros(n-N)\n    for i in range(n-N):\n        z1[i] = (x1[i+N] - x1[i]) / N  # Mean sequence\n    B = np.vstack([-z1, np.ones(n-N)]).T\n    Y = x0[N:]\n    u = np.linalg.lstsq(B, Y, rcond=None)[0]\n    a, b = u\n    f = lambda k: (x0[0] - b/a) * np.exp(-a * k) + b/a\n    x0_hat = np.array([f(k) for k in range(n)])\n    return x0_hat, a, b\n\n\n# Forecast function\ndef forecast_gm11(model, history, steps):\n    x0_hat, a, b = model(history)\n    f = lambda k: (history[0] - b/a) * np.exp(-a * k) + b/a\n    future_forecast = np.array([f(k + len(history)) for k in range(steps)])\n    return future_forecast\n\n\ndef forecast_gm1n(model, history, steps, N):\n    x0_hat, a, b = model(history, N)\n    f = lambda k: (history[0] - b/a) * np.exp(-a * k) + b/a\n    future_forecast = np.array([f(k + len(history)) for k in range(steps)])\n    return future_forecast\n\n\n# Perform iterative forecasting\ndef iterative_forecast_gm11(train, test, step_size):\n    history = list(train)\n    predictions = []\n    for i in tqdm_notebook(range(0, len(test), step_size)):\n        step_forecast = forecast_gm11(gm11, history, step_size)\n        predictions.extend(step_forecast[:min(step_size, len(test) - i)])\n        history.extend(test[i:i + step_size])\n    return np.array(predictions)\n\n\n# Perform iterative forecasting for GM(1,N)\ndef iterative_forecast_gm1n(train, test, step_size, N):\n    history = list(train)\n    predictions = []\n    for i in tqdm_notebook(range(0, len(test), step_size)):\n        step_forecast = forecast_gm1n(gm1n, history, step_size, N)\n        predictions.extend(step_forecast[:min(step_size, len(test) - i)])\n        history.extend(test[i:i + step_size])\n    return np.array(predictions)","metadata":{"execution":{"iopub.status.busy":"2024-06-22T13:46:31.292398Z","iopub.execute_input":"2024-06-22T13:46:31.29297Z","iopub.status.idle":"2024-06-22T13:46:31.32038Z","shell.execute_reply.started":"2024-06-22T13:46:31.292919Z","shell.execute_reply":"2024-06-22T13:46:31.318956Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"markdown","source":"# Code","metadata":{}},{"cell_type":"code","source":"df = pd.read_csv(DATASET_PATH)\n\n# Convert 'date' and 'time' column data types\ndf['date'] = pd.to_datetime(df['date'])\ndf['time'] = pd.to_datetime(df['time'], format='%I:%M %p').dt.time\n\n# Combine 'date' and 'time' columns into a single datetime column\ndf['datetime'] = df.apply(lambda row: datetime.combine(row['date'], row['time']), axis=1)\n\n# Drop the original 'date' and 'time' columns as they are no longer needed\ndf = df.drop(columns=['date', 'time'])\n\n# Filter data from Jan'24 till May'24\nstart_date = '2024-01-01'\ndf = df[(df['datetime'] >= start_date)]\n\ndf.head(3)","metadata":{"execution":{"iopub.status.busy":"2024-06-22T13:46:31.32245Z","iopub.execute_input":"2024-06-22T13:46:31.323227Z","iopub.status.idle":"2024-06-22T13:46:31.762716Z","shell.execute_reply.started":"2024-06-22T13:46:31.323176Z","shell.execute_reply":"2024-06-22T13:46:31.761425Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"      temperature  dew_point  humidity wind  wind_speed  wind_gust  pressure  \\\n1418           61         37        42  ENE           5          0     29.98   \n1419           61         37        42    N           5          0     29.95   \n1420           61         37        42  NNE           5          0     29.95   \n\n      precip condition     city   country            datetime  \n1418     0.0      Haze  Karachi  Pakistan 2024-01-01 00:00:00  \n1419     0.0      Haze  Karachi  Pakistan 2024-01-01 00:30:00  \n1420     0.0      Haze  Karachi  Pakistan 2024-01-01 01:00:00  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>temperature</th>\n      <th>dew_point</th>\n      <th>humidity</th>\n      <th>wind</th>\n      <th>wind_speed</th>\n      <th>wind_gust</th>\n      <th>pressure</th>\n      <th>precip</th>\n      <th>condition</th>\n      <th>city</th>\n      <th>country</th>\n      <th>datetime</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1418</th>\n      <td>61</td>\n      <td>37</td>\n      <td>42</td>\n      <td>ENE</td>\n      <td>5</td>\n      <td>0</td>\n      <td>29.98</td>\n      <td>0.0</td>\n      <td>Haze</td>\n      <td>Karachi</td>\n      <td>Pakistan</td>\n      <td>2024-01-01 00:00:00</td>\n    </tr>\n    <tr>\n      <th>1419</th>\n      <td>61</td>\n      <td>37</td>\n      <td>42</td>\n      <td>N</td>\n      <td>5</td>\n      <td>0</td>\n      <td>29.95</td>\n      <td>0.0</td>\n      <td>Haze</td>\n      <td>Karachi</td>\n      <td>Pakistan</td>\n      <td>2024-01-01 00:30:00</td>\n    </tr>\n    <tr>\n      <th>1420</th>\n      <td>61</td>\n      <td>37</td>\n      <td>42</td>\n      <td>NNE</td>\n      <td>5</td>\n      <td>0</td>\n      <td>29.95</td>\n      <td>0.0</td>\n      <td>Haze</td>\n      <td>Karachi</td>\n      <td>Pakistan</td>\n      <td>2024-01-01 01:00:00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Apply Differencing & Remove Outliers","metadata":{}},{"cell_type":"code","source":"df['temperature_diff'] = df['temperature'].diff().dropna()","metadata":{"execution":{"iopub.status.busy":"2024-06-22T13:46:31.764151Z","iopub.execute_input":"2024-06-22T13:46:31.764579Z","iopub.status.idle":"2024-06-22T13:46:31.777344Z","shell.execute_reply.started":"2024-06-22T13:46:31.764542Z","shell.execute_reply":"2024-06-22T13:46:31.775749Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"markdown","source":"## Splitting the dataset & fit GM(1,1)","metadata":{}},{"cell_type":"code","source":"df_train = df[(df['datetime'] < \"2024-05-25\")]\ndf_test = df[(df['datetime'] > \"2024-05-25\")]\n\n# Prepare the differenced temperature data\ntemperature_diff_train = df_train['temperature_diff'].dropna()\ntemperature_diff_test = df_test['temperature_diff'].dropna()\n\nprint(f'Train data size: {temperature_diff_train.shape}')\nprint(f'Test data size: {temperature_diff_test.shape}')","metadata":{"execution":{"iopub.status.busy":"2024-06-22T13:46:31.778878Z","iopub.execute_input":"2024-06-22T13:46:31.779281Z","iopub.status.idle":"2024-06-22T13:46:31.794112Z","shell.execute_reply.started":"2024-06-22T13:46:31.779251Z","shell.execute_reply":"2024-06-22T13:46:31.792557Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"Train data size: (6901,)\nTest data size: (335,)\n","output_type":"stream"}]},{"cell_type":"code","source":"# Perform the forecasting\nstep_size = 3\npredictions = iterative_forecast_gm11(temperature_diff_train, temperature_diff_test, step_size)","metadata":{"execution":{"iopub.status.busy":"2024-06-22T13:46:31.795697Z","iopub.execute_input":"2024-06-22T13:46:31.796112Z","iopub.status.idle":"2024-06-22T13:46:34.599142Z","shell.execute_reply.started":"2024-06-22T13:46:31.796073Z","shell.execute_reply":"2024-06-22T13:46:34.59743Z"},"trusted":true},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"  0%|          | 0/112 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"144de6613e664b08b13f5db1d724a099"}},"metadata":{}}]},{"cell_type":"code","source":"results_df = pd.DataFrame({\n    'datetime': df_test['datetime'].values[:len(predictions)],\n    'actual': temperature_diff_test[:len(predictions)],\n    'predicted': predictions\n})","metadata":{"execution":{"iopub.status.busy":"2024-06-22T13:46:34.600888Z","iopub.execute_input":"2024-06-22T13:46:34.601414Z","iopub.status.idle":"2024-06-22T13:46:34.610467Z","shell.execute_reply.started":"2024-06-22T13:46:34.601369Z","shell.execute_reply":"2024-06-22T13:46:34.608987Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"# Plotting actual vs predicted values\nplt.figure(figsize=(10, 6))\n\nplt.plot(results_df['datetime'], results_df['actual'].values, label='Actual')\nplt.plot(results_df['datetime'], results_df['predicted'], label='Predicted', linestyle='--')\nplt.xlabel('Datetime')\nplt.ylabel('Temperature Difference')\nplt.title('Actual vs Predicted Temperature Difference')\nplt.legend()\nplt.grid(True)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-22T13:46:34.613556Z","iopub.execute_input":"2024-06-22T13:46:34.613985Z","iopub.status.idle":"2024-06-22T13:46:35.075119Z","shell.execute_reply.started":"2024-06-22T13:46:34.613947Z","shell.execute_reply":"2024-06-22T13:46:35.07386Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"## Accuracy Metrics","metadata":{}},{"cell_type":"code","source":"mse = ((results_df['actual'] - results_df['predicted']) ** 2).mean()\nmse","metadata":{"execution":{"iopub.status.busy":"2024-06-22T13:48:27.993737Z","iopub.execute_input":"2024-06-22T13:48:27.994193Z","iopub.status.idle":"2024-06-22T13:48:28.003414Z","shell.execute_reply.started":"2024-06-22T13:48:27.994159Z","shell.execute_reply":"2024-06-22T13:48:28.001805Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"945.136271417819"},"metadata":{}}]},{"cell_type":"markdown","source":"## Saving results","metadata":{}},{"cell_type":"code","source":"# Assuming temperature_diff_test.index and forecasts are defined\nindex_values = results_df.index\nforecast_values = results_df.predicted.values\n\n# Create a DataFrame from the index and list values\ndf_forecast = pd.DataFrame({'Index': index_values, 'Datetime': results_df.datetime, 'Actual': results_df.actual, 'Forecasts': forecast_values})\ndf_forecast.head()\n\n# Save the DataFrame to a CSV file\ndf_forecast.to_csv('GM_Forecasts.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2024-06-22T13:51:15.683757Z","iopub.execute_input":"2024-06-22T13:51:15.684597Z","iopub.status.idle":"2024-06-22T13:51:15.700561Z","shell.execute_reply.started":"2024-06-22T13:51:15.684559Z","shell.execute_reply":"2024-06-22T13:51:15.699223Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}