time-series-forecasting-model-comparisons.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":8758825,"sourceType":"datasetVersion","datasetId":5135614}],"dockerImageVersionId":30732,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Imports","metadata":{}},{"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\n\nwarnings.filterwarnings('ignore')","metadata":{"execution":{"iopub.status.busy":"2024-06-29T17:13:26.544186Z","iopub.execute_input":"2024-06-29T17:13:26.544591Z","iopub.status.idle":"2024-06-29T17:13:27.702096Z","shell.execute_reply.started":"2024-06-29T17:13:26.544535Z","shell.execute_reply":"2024-06-29T17:13:27.700849Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":"# Constant","metadata":{}},{"cell_type":"code","source":"ARIMA_PREDICTIONS = \"/kaggle/input/weather-underground/ARIMA_Forecasts_Datetime.csv\"\nGM_PREDICTIONS = \"/kaggle/input/weather-underground/GM_Forecasts.csv\"","metadata":{"execution":{"iopub.status.busy":"2024-06-29T17:13:27.704050Z","iopub.execute_input":"2024-06-29T17:13:27.704523Z","iopub.status.idle":"2024-06-29T17:13:27.709829Z","shell.execute_reply.started":"2024-06-29T17:13:27.704492Z","shell.execute_reply":"2024-06-29T17:13:27.708539Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"# Code","metadata":{}},{"cell_type":"code","source":"df_arima_predictions = pd.read_csv(ARIMA_PREDICTIONS)\ndf_gm_predictions = pd.read_csv(GM_PREDICTIONS)","metadata":{"execution":{"iopub.status.busy":"2024-06-29T17:13:27.717437Z","iopub.execute_input":"2024-06-29T17:13:27.717897Z","iopub.status.idle":"2024-06-29T17:13:27.743749Z","shell.execute_reply.started":"2024-06-29T17:13:27.717858Z","shell.execute_reply":"2024-06-29T17:13:27.742383Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"df_arima_predictions = df_arima_predictions.rename(columns={'Forecasts': 'ARIMA_Forecast'})\ndf_arima_predictions.head(3)","metadata":{"execution":{"iopub.status.busy":"2024-06-29T17:13:27.745169Z","iopub.execute_input":"2024-06-29T17:13:27.745642Z","iopub.status.idle":"2024-06-29T17:13:27.771966Z","shell.execute_reply.started":"2024-06-29T17:13:27.745600Z","shell.execute_reply":"2024-06-29T17:13:27.770624Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"   Index  ARIMA_Forecast\n0   8321       -0.487280\n1   8322       -0.318901\n2   8323       -0.252342","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>Index</th>\n      <th>ARIMA_Forecast</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>8321</td>\n      <td>-0.487280</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>8322</td>\n      <td>-0.318901</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8323</td>\n      <td>-0.252342</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_gm_predictions = df_gm_predictions.rename(columns={'Forecasts': 'GM_Forecast', 'Actual': 'Temp_Diff'})\ndf_gm_predictions['Datetime'] = pd.to_datetime(df_gm_predictions['Datetime'])\ndf_gm_predictions.head(3)","metadata":{"execution":{"iopub.status.busy":"2024-06-29T17:13:27.773886Z","iopub.execute_input":"2024-06-29T17:13:27.774286Z","iopub.status.idle":"2024-06-29T17:13:27.801818Z","shell.execute_reply.started":"2024-06-29T17:13:27.774252Z","shell.execute_reply":"2024-06-29T17:13:27.800401Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"   Index            Datetime  Temp_Diff  GM_Forecast\n0   8321 2024-05-25 00:30:00        0.0    25.905326\n1   8322 2024-05-25 01:00:00        0.0    25.907901\n2   8323 2024-05-25 01:30:00        0.0    25.910475","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>Index</th>\n      <th>Datetime</th>\n      <th>Temp_Diff</th>\n      <th>GM_Forecast</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>8321</td>\n      <td>2024-05-25 00:30:00</td>\n      <td>0.0</td>\n      <td>25.905326</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>8322</td>\n      <td>2024-05-25 01:00:00</td>\n      <td>0.0</td>\n      <td>25.907901</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8323</td>\n      <td>2024-05-25 01:30:00</td>\n      <td>0.0</td>\n      <td>25.910475</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_merged_forecasts = pd.merge(df_arima_predictions, df_gm_predictions, on=['Index'], how='inner')\ndf_merged_forecasts = df_merged_forecasts[['Index', 'Datetime', 'Temp_Diff', 'ARIMA_Forecast', 'GM_Forecast']]\ndf_merged_forecasts","metadata":{"execution":{"iopub.status.busy":"2024-06-29T17:13:27.803901Z","iopub.execute_input":"2024-06-29T17:13:27.804293Z","iopub.status.idle":"2024-06-29T17:13:27.836445Z","shell.execute_reply.started":"2024-06-29T17:13:27.804258Z","shell.execute_reply":"2024-06-29T17:13:27.835233Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"     Index            Datetime  Temp_Diff  ARIMA_Forecast  GM_Forecast\n0     8321 2024-05-25 00:30:00        0.0       -0.487280    25.905326\n1     8322 2024-05-25 01:00:00        0.0       -0.318901    25.907901\n2     8323 2024-05-25 01:30:00        0.0       -0.252342    25.910475\n3     8324 2024-05-25 02:00:00        0.0       -0.163692    25.907215\n4     8325 2024-05-25 02:30:00        0.0       -0.060384    25.909787\n..     ...                 ...        ...             ...          ...\n329   8651 2024-05-31 21:30:00        0.0       -0.545684    27.669459\n330   8652 2024-05-31 22:00:00        0.0       -0.519263    27.672173\n331   8653 2024-05-31 22:30:00        0.0       -0.392363    27.674887\n332   8654 2024-05-31 23:00:00       -2.0       -0.377810    27.671237\n333   8655 2024-05-31 23:30:00        0.0       -0.315892    27.673949\n\n[334 rows x 5 columns]","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>Index</th>\n      <th>Datetime</th>\n      <th>Temp_Diff</th>\n      <th>ARIMA_Forecast</th>\n      <th>GM_Forecast</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>8321</td>\n      <td>2024-05-25 00:30:00</td>\n      <td>0.0</td>\n      <td>-0.487280</td>\n      <td>25.905326</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>8322</td>\n      <td>2024-05-25 01:00:00</td>\n      <td>0.0</td>\n      <td>-0.318901</td>\n      <td>25.907901</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8323</td>\n      <td>2024-05-25 01:30:00</td>\n      <td>0.0</td>\n      <td>-0.252342</td>\n      <td>25.910475</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>8324</td>\n      <td>2024-05-25 02:00:00</td>\n      <td>0.0</td>\n      <td>-0.163692</td>\n      <td>25.907215</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>8325</td>\n      <td>2024-05-25 02:30:00</td>\n      <td>0.0</td>\n      <td>-0.060384</td>\n      <td>25.909787</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>329</th>\n      <td>8651</td>\n      <td>2024-05-31 21:30:00</td>\n      <td>0.0</td>\n      <td>-0.545684</td>\n      <td>27.669459</td>\n    </tr>\n    <tr>\n      <th>330</th>\n      <td>8652</td>\n      <td>2024-05-31 22:00:00</td>\n      <td>0.0</td>\n      <td>-0.519263</td>\n      <td>27.672173</td>\n    </tr>\n    <tr>\n      <th>331</th>\n      <td>8653</td>\n      <td>2024-05-31 22:30:00</td>\n      <td>0.0</td>\n      <td>-0.392363</td>\n      <td>27.674887</td>\n    </tr>\n    <tr>\n      <th>332</th>\n      <td>8654</td>\n      <td>2024-05-31 23:00:00</td>\n      <td>-2.0</td>\n      <td>-0.377810</td>\n      <td>27.671237</td>\n    </tr>\n    <tr>\n      <th>333</th>\n      <td>8655</td>\n      <td>2024-05-31 23:30:00</td>\n      <td>0.0</td>\n      <td>-0.315892</td>\n      <td>27.673949</td>\n    </tr>\n  </tbody>\n</table>\n<p>334 rows × 5 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Plotting\nplt.figure(figsize=(18, 5))\n\nplt.plot(df_merged_forecasts['Datetime'], df_merged_forecasts['Temp_Diff'], label='Actual Temp')\nplt.plot(df_merged_forecasts['Datetime'], df_merged_forecasts['ARIMA_Forecast'], label='ARIMA Forecast', linestyle='--', color='red')\nplt.plot(df_merged_forecasts['Datetime'], df_merged_forecasts['GM_Forecast'], label='GM Forecast', linestyle='--', color='green')\n\nplt.xlabel('Datetime')\nplt.ylabel('Temperature Diff (F)')\nplt.title('Temperature Forecasts (ARIMA VS GM)')\nplt.legend()\nplt.grid(True)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-29T17:13:27.838119Z","iopub.execute_input":"2024-06-29T17:13:27.838575Z","iopub.status.idle":"2024-06-29T17:13:28.315417Z","shell.execute_reply.started":"2024-06-29T17:13:27.838512Z","shell.execute_reply":"2024-06-29T17:13:28.314062Z"},"trusted":true},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1800x500 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"## MSE Comparison","metadata":{}},{"cell_type":"code","source":"mse_arima = ((df_merged_forecasts['Temp_Diff'] - df_merged_forecasts['ARIMA_Forecast']) ** 2).mean()\nmse_gm = ((df_merged_forecasts['Temp_Diff'] - df_merged_forecasts['GM_Forecast']) ** 2).mean()\n\nprint(f'MSE ARIMA: {mse_arima} VS MSE GM: {mse_gm}')","metadata":{"execution":{"iopub.status.busy":"2024-06-29T17:13:28.316946Z","iopub.execute_input":"2024-06-29T17:13:28.317359Z","iopub.status.idle":"2024-06-29T17:13:28.328398Z","shell.execute_reply.started":"2024-06-29T17:13:28.317322Z","shell.execute_reply":"2024-06-29T17:13:28.326962Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"MSE ARIMA: 23.54000157443077 VS MSE GM: 907.9506190836173\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<!DOCTYPE html>\n<html lang=\"en\">\n<head>\n    <meta charset=\"UTF-8\">\n    <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n    <style>\n        body {\n            font-family: Arial, sans-serif;\n            line-height: 1.6;\n            background-color: #f4f4f9;\n            color: #333;\n            margin: 0;\n            padding: 20px;\n        }\n        .container {\n            max-width: 800px;\n            margin: auto;\n            background: #fff;\n            padding: 20px;\n            border-radius: 8px;\n            box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);\n        }\n        h1 {\n            text-align: center;\n            color: #0056b3;\n        }\n        .conclusion {\n            margin-top: 20px;\n        }\n        .conclusion h2 {\n            color: #0056b3;\n        }\n        .conclusion p {\n            margin-bottom: 10px;\n        }\n        .highlight {\n            color: #e74c3c;\n            font-weight: bold;\n        }\n        .note {\n            background-color: #fffbcc;\n            padding: 10px;\n            border-left: 5px solid #f1c40f;\n            margin-top: 20px;\n        }\n    </style>\n    <title>Model Assessment Conclusion</title>\n</head>\n<body>\n    <div class=\"container\">\n        <h1>Model Assessment Conclusion</h1>\n        <div class=\"conclusion\">\n            <h2>Observations and Insights</h2>\n            <p>The Mean Squared Error (MSE) is a measure of the average squared difference between the observed actual outcomes and the predictions provided by a model. In this assessment, we compared the performance of two forecasting models: ARIMA and GM.</p>\n            <p>The MSE for the <span class=\"highlight\">ARIMA</span> model is <span class=\"highlight\">23.54</span>, while the MSE for the <span class=\"highlight\">GM</span> model is significantly higher at <span class=\"highlight\">907.95</span>.</p>\n            <h2>Conclusion</h2>\n            <p>Based on the MSE values, it is evident that the ARIMA model performs considerably better than the GM model. The lower MSE indicates that the ARIMA model's predictions are much closer to the actual observed values compared to the GM model.</p>\n            <h2>Concerns</h2>\n            <p>There are some concerns regarding the performance of the GM model:</p>\n            <ul>\n                <li>The high MSE value of the GM model suggests that its predictions are highly inaccurate, leading to potential issues if used in a real-world application.</li>\n                <li>The large discrepancy between the MSE values of the ARIMA and GM models highlights a need for further investigation into the reasons behind the poor performance of the GM model.</li>\n            </ul>\n            <div class=\"note\">\n                <strong>Note:</strong> It is crucial to continually validate and test forecasting models with new data to ensure their reliability and accuracy over time.\n            </div>\n        </div>\n    </div>\n</body>\n</html>\n","metadata":{}}]}