ARMA_CO2_forecast.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "be3c8034-7aa8-4056-ae3b-95d4f2d57406",
   "metadata": {},
   "source": [
    "### Getting Started;\n",
    "\n",
    "This code imports essential libraries for time series analysis and modeling. Below is a breakdown of the key libraries and their respective purposes:\n",
    "\n",
    "- **`time`, `itertools`, and `warnings`**: These utility libraries are used for time measurement, generating combinations of model parameters, and controlling warnings. The `time` library measures execution time, while `itertools` helps in generating parameter combinations, and `warnings` manages the suppression of warnings during code execution.\n",
    "\n",
    "- **`matplotlib.pyplot` and `numpy`**: These libraries are used for data visualization and numerical operations. `matplotlib.pyplot` is used to plot graphs, whereas `numpy` provides fundamental tools for handling numerical data.\n",
    "\n",
    "- **`pandas`**: This library is used for data manipulation, allowing efficient handling of time series data. It provides data structures like DataFrames that are key for preprocessing and manipulating datasets before feeding them into models.\n",
    "\n",
    "- **`statsmodels.api` and related modules**: These are the core libraries for statistical modeling in time series analysis. Specifically, they provide:\n",
    "  - **ARIMA (AutoRegressive Integrated Moving Average)** models for time series forecasting.\n",
    "  - **Holt-Winters** exponential smoothing models for capturing trends and seasonality.\n",
    "  - **SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors)** for seasonal forecasting with external variables.\n",
    "  \n",
    "- **`mean_absolute_error`**: This metric from `sklearn.metrics` is used to evaluate the accuracy of predictive models by calculating the average magnitude of the errors in a set of predictions, providing a measure of how well the model performs.\n",
    "\n",
    "- **`warnings.filterwarnings('ignore')`**: This command suppresses warning messages that might otherwise clutter the output, ensuring cleaner execution logs and focusing on the essential outputs.\n",
    "\n",
    "### Purpose\n",
    "\n",
    "By utilizing these libraries, the code is designed to perform time series forecasting and model evaluation, with a focus on statistical and machine learning methods. These libraries provide the tools needed for data preprocessing, model selection, and performance evaluation, all of which are essential in time series analysis tasks such as trend forecasting, anomaly detection, and seasonal prediction.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "26336d23-1fe2-40e3-8ac9-ad940d5fd40b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import time\n",
    "import itertools\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n",
    "from statsmodels.tsa.holtwinters import SimpleExpSmoothing\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "import statsmodels.tsa.api as smt\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e3970c-ef7c-41b5-b4ab-72479a19a414",
   "metadata": {},
   "source": [
    "### Explanation;\n",
    "\n",
    "The code provided performs a time series data preparation and evaluation process, specifically for the atmospheric CO2 levels measured at Mauna Loa Observatory, Hawaii, from March 1958 to December 2001. Below is a breakdown of the key steps and processes involved:\n",
    "\n",
    "1. **Loading the Data**: \n",
    "   - The dataset used in this analysis is the **Mauna Loa CO2 dataset**, which is part of the `statsmodels` library.\n",
    "   - `sm.datasets.co2.load_pandas()` is used to load the data as a pandas DataFrame. The dataset contains atmospheric CO2 measurements taken at the Mauna Loa Observatory.\n",
    "\n",
    "2. **Data Preprocessing**:\n",
    "   - The CO2 levels are extracted from the DataFrame with `y = data.data['co2']`.\n",
    "   - **Resampling the Data**: The `resample('MS').mean()` method is used to resample the data to a monthly frequency, taking the mean CO2 levels for each month. This helps to aggregate the data from the daily observations into monthly data points, which is commonly done in time series analysis to reduce the noise and capture the underlying trend.\n",
    "   - **Handling Missing Values**: Missing values in the dataset are filled using the `fillna(y.bfill())` method. The `bfill()` function performs backward filling, meaning that missing values are replaced with the next valid data point in the series.\n",
    "   \n",
    "3. **Splitting the Data into Training and Testing Sets**:\n",
    "   - The data is then split into two subsets: the **training set** and the **testing set**.\n",
    "   - The training set (`train`) includes all data up to and including December 1, 1997. This period is used to build and train a forecasting model.\n",
    "   - The testing set (`test`) includes the data from January 1, 1998, onwards. This subset is used for model validation, where the model's predictions will be compared to actual CO2 levels in the test period.\n",
    "\n",
    "4. **Measuring Execution Time**:\n",
    "   - The code measures the time taken to execute the data preprocessing and splitting operations using the `time` module.\n",
    "   - The `end_time = time.time()` function captures the current time when the operations are finished.\n",
    "   - The `elapsed_time = end_time - start_time` calculates the time difference between the start and end of the execution, providing the total time taken for the operations to complete.\n",
    "   - Finally, the elapsed time is printed to the console with the format `Elapsed time: X seconds`, where X represents the time taken for the execution.\n",
    "\n",
    "### Purpose;\n",
    "\n",
    "This code prepares the CO2 data for time series forecasting by:\n",
    "- Aggregating the data into monthly averages.\n",
    "- Handling missing values appropriately to ensure the dataset is complete.\n",
    "- Splitting the data into a training set and a testing set, allowing for model training and validation.\n",
    "- Measuring the time taken to complete the preprocessing steps, which is useful for optimizing the workflow.\n",
    "\n",
    "These steps are typical in time series analysis and are essential for building predictive models that can forecast future CO2 levels based on historical data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9f959793-1f87-4681-824c-c09aae7e32ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time: 1729471543.1376 seconds\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Atmospheric CO2 from Continuous Air Samples at Mauna Loa Observatory, Hawaii, U.S.A.;\n",
    "# Period of Record: March 1958 - December 2001;\n",
    "data = sm.datasets.co2.load_pandas()\n",
    "y = data.data\n",
    "y = y['co2'].resample('MS').mean()\n",
    "y = y.fillna(y.bfill())\n",
    "train = y[:'1997-12-01']\n",
    "test = y['1998-01-01':]\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time\n",
    "print(f\"Elapsed time: {elapsed_time:.4f} seconds\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5ae3ca6-0d03-44cb-8bff-29cd165353c4",
   "metadata": {},
   "source": [
    "#### Training the ARIMA Model and Making Forecasts;\n",
    "Configured with the parameters Order (1,1,1), this ARIMA model utilizes 1 lag (autoregressive) term, first-order differencing for stationarity, and a moving average process with a window of 1. The model's predictions for 48 steps are reordered based on the test set's time index. The training data, test data, and predictions are visualized, with the results expressed as the MAE (Mean Absolute Error) error rate. The time taken for model training is also calculated and displayed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4acbcae5-0332-4194-b258-3e99f1d58a88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time: 1729472873.1983 seconds\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# ARIMA(p, d, q): (Autoregressive Integrated Moving Average);\n",
    "model = sm.tsa.arima.ARIMA(train, order=(1,1,1))\n",
    "arima_model = model.fit()\n",
    "arima_model.summary()\n",
    "y_pred = arima_model.get_forecast(steps=48).predicted_mean\n",
    "y_pred = pd.Series(y_pred, index=test.index)\n",
    "def plot_co2(train, test, y_pred, title):\n",
    "    mae = mean_absolute_error(test, y_pred)\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    ax = train[\"1985\":].plot(legend=True, label=\"TRAIN\", color='blue', linewidth=2, linestyle='-', alpha=0.75)\n",
    "    test.plot(legend=True, label=\"TEST\", color='green', linewidth=2, linestyle='-', alpha=0.75)\n",
    "    y_pred.plot(legend=True, label=\"PREDICTION\", color='red', linewidth=2, linestyle='--')\n",
    "    plt.title(f\"{title} - CO2 Concentration Forecast (MAE={round(mae, 2)})\", fontsize=14, fontweight='bold')\n",
    "    plt.xlabel('Date', fontsize=8)\n",
    "    plt.ylabel('CO2 Concentration (ppm)', fontsize=12)\n",
    "    plt.grid(True, linestyle=':', alpha=0.7)\n",
    "    plt.xticks(rotation=45, ha='right', fontsize=10)\n",
    "    plt.yticks(fontsize=8)\n",
    "    plt.legend(loc='upper left', fontsize=12)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "plot_co2(train, test, y_pred, \"ARIMA\")\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time\n",
    "print(f\"Elapsed time: {elapsed_time:.4f} seconds\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c99e8f7-571f-4429-96ce-9dedbaa4a29c",
   "metadata": {},
   "source": [
    "# Hyperparameter Optimization for ARIMA Model using AIC;\n",
    "\n",
    "This code performs hyperparameter optimization for an ARIMA model using the **AIC (Akaike Information Criterion)**. The objective is to determine the best ARIMA model parameters—**p**, **d**, and **q**—based on their AIC values:\n",
    "\n",
    "- **p**: Autoregressive (AR) order  \n",
    "- **d**: Degree of differencing to achieve stationarity  \n",
    "- **q**: Moving average (MA) order  \n",
    "\n",
    "The output produced by the code shows the AIC values for different ARIMA models with varying parameter combinations. Here's how to interpret the results:\n",
    "\n",
    "This process is a standard example of hyperparameter optimization for ARIMA models, where the goal is to minimize the AIC to identify the most suitable model. The **ARIMA(2, 1, 3)** model stands out as the best choice based on AIC, indicating it provides the best fit for the training data. \n",
    "\n",
    "However, the **elapsed time** reported seems erroneous and should be re-evaluated as it is excessively large for this type of task.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "29223969-339e-4532-9398-a946275d04f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best ARIMA(0, 0, 0) AIC=3954.01\n",
      "Best ARIMA(0, 0, 1) AIC=3312.73\n",
      "Best ARIMA(0, 0, 2) AIC=2771.31\n",
      "Best ARIMA(0, 0, 3) AIC=2328.74\n",
      "Best ARIMA(0, 1, 0) AIC=1523.91\n",
      "Best ARIMA(0, 1, 1) AIC=1254.79\n",
      "Best ARIMA(0, 1, 2) AIC=1137.97\n",
      "Best ARIMA(0, 1, 3) AIC=1094.51\n",
      "Best ARIMA(2, 1, 1) AIC=979.80\n",
      "Best ARIMA(2, 1, 2) AIC=979.50\n",
      "Best ARIMA(2, 1, 3) AIC=843.95\n",
      "Elapsed time: 1729472101.3301 seconds\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Hyperparameter Optimization (Model Derecelerini Belirleme);\n",
    "# # AIC & BIC İstatistiklerine Göre Model Derecesini Belirleme;\n",
    "p = d = q = range(0, 4)\n",
    "pdq = list(itertools.product(p, d, q))\n",
    "def arima_optimizer_aic(train, orders):\n",
    "    best_aic, best_params = float(\"inf\"), None\n",
    "    for order in orders:\n",
    "        try:\n",
    "            arima_model = sm.tsa.ARIMA(train, order=order).fit()\n",
    "            aic = arima_model.aic\n",
    "            if aic < best_aic:\n",
    "                best_aic, best_params = aic, order\n",
    "                print('Best ARIMA{} AIC={:.2f}'.format(best_params, best_aic))\n",
    "        except Exception as e:\n",
    "            print(f\"Error for order {order}: {e}\")\n",
    "    return best_params\n",
    "best_params_aic = arima_optimizer_aic(train, pdq)\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time\n",
    "print(f\"Elapsed time: {elapsed_time:.4f} seconds\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ba762c1-f945-46a6-96ff-47d0365d655a",
   "metadata": {},
   "source": [
    "The ARIMA model is trained using the best hyperparameters based on the AIC criterion. The model is built with the optimal parameters determined by AIC, and after training, it is made ready for testing and forecasting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a51ba7b7-67a5-4bbf-90c9-b33aeee4f725",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time: 1729472229.2418 seconds\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Final Model;\n",
    "# Fit the final ARIMA model using the best hyperparameters selected by AIC;\n",
    "arima_model = sm.tsa.ARIMA(train, order=best_params_aic).fit()\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time\n",
    "print(f\"Elapsed time: {elapsed_time:.4f} seconds\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20a42b65-12e1-4091-b475-7833791ec587",
   "metadata": {},
   "source": [
    "The predictions for the test set using the ARIMA model are assigned to the y_pred variable, with a prediction made for each data point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cdf709c6-d1b0-4a3d-aa61-ebbd45c98419",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time: 1729472297.8255 seconds\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Make Predictions for the testset;\n",
    "y_pred = arima_model.forecast(steps=len(test))\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time\n",
    "print(f\"Elapsed time: {elapsed_time:.4f} seconds\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "947d6e6a-0b7a-48ee-a9e4-89d68842701f",
   "metadata": {},
   "source": [
    "This line of code converts the y_pred predictions into a pandas Series, ensuring that the predictions align with the correct index from the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "89635c80-3986-49c8-959e-a56cdee9426d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time: 1729472326.9539 seconds\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Convert predictions to a pandas Series with the correct index;\n",
    "y_pred = pd.Series(y_pred, index=test.index)\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time\n",
    "print(f\"Elapsed time: {elapsed_time:.4f} seconds\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8524c82a-f3e4-475b-a57b-b246fdb358e8",
   "metadata": {},
   "source": [
    "The training data, test data, and the predictions made by the ARIMA model on the same graph. It also measures and outputs the time taken to run this section of the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e851dab3-190b-49a9-ad98-c0bbfbb204f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time: 1729472854.3441 seconds\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Plot the actual data and the predictions;\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.plot(train, label='Training Set', color='royalblue', linewidth=2, linestyle='-', alpha=0.8)\n",
    "plt.plot(test, label='Test Set', color='darkgreen', linewidth=2, linestyle='-', alpha=0.8)\n",
    "plt.plot(y_pred, label='ARIMA Predictions', color='darkred', linewidth=2, linestyle='--', alpha=0.8)\n",
    "plt.legend(loc='upper left', fontsize=10, frameon=True, framealpha=0.7)\n",
    "plt.xlabel('Year', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('CO2 Levels (ppm)', fontsize=14, fontweight='bold')\n",
    "plt.title('ARIMA Model Forecasting for CO2 Concentrations', fontsize=16, fontweight='bold')\n",
    "plt.grid(True, linestyle=':', color='gray', alpha=0.6)\n",
    "plt.xticks(rotation=45, ha='right', fontsize=10)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time\n",
    "print(f\"Elapsed time: {elapsed_time:.4f} seconds\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e452a8a8-8a09-4b0d-8ddf-ddf313771e42",
   "metadata": {},
   "source": [
    "Generate a professional and clear plot to visualize the ARIMA model's forecasting results for CO2 concentrations, comparing the training data, test data, and ARIMA predictions. Here is a breakdown of each part:\n",
    "\n",
    "1. **Plot Figure Initialization**:\n",
    "   - `plt.figure(figsize=(12, 8))`: Creates a figure with a size of 12x8 inches, providing a wider view for better clarity and resolution of the plot.\n",
    "\n",
    "2. **Plotting the Training Data**:\n",
    "   - `plt.plot(train, label='Training Set', color='royalblue', linewidth=2, linestyle='-', alpha=0.8)`: Plots the training dataset with the label \"Training Set.\" The training data is visualized with a solid line (`linestyle='-'`), a royal blue color (`color='royalblue'`), a line width of 2 (`linewidth=2`), and a transparency level of 0.8 (`alpha=0.8`).\n",
    "\n",
    "3. **Plotting the Test Data**:\n",
    "   - `plt.plot(test, label='Test Set', color='darkgreen', linewidth=2, linestyle='-', alpha=0.8)`: Plots the test dataset with the label \"Test Set.\" The test data is visualized similarly to the training set, but with a dark green color (`color='darkgreen'`).\n",
    "\n",
    "4. **Plotting the ARIMA Predictions**:\n",
    "   - `plt.plot(y_pred, label='ARIMA Predictions', color='darkred', linewidth=2, linestyle='--', alpha=0.8)`: This plots the predicted values from the ARIMA model with the label \"ARIMA Predictions.\" The predictions are shown using a dashed line (`linestyle='--'`), a dark red color (`color='darkred'`), and similar line width and transparency as the previous plots.\n",
    "\n",
    "5. **Legend Customization**:\n",
    "   - `plt.legend(loc='upper left', fontsize=12, frameon=True, framealpha=0.7)`: Adds a legend in the upper left corner, with the font size set to 12 for readability. The legend has a semi-transparent background (`framealpha=0.7`) for better visibility.\n",
    "\n",
    "6. **Axis Labels**:\n",
    "   - `plt.xlabel('Year', fontsize=14, fontweight='bold')`: Sets the x-axis label to \"Year\" with bold font weight and a font size of 14.\n",
    "   - `plt.ylabel('CO2 Levels (ppm)', fontsize=14, fontweight='bold')`: Sets the y-axis label to \"CO2 Levels (ppm)\" with bold font weight and a font size of 14.\n",
    "\n",
    "7. **Title Customization**:\n",
    "   - `plt.title('ARIMA Model Forecasting for CO2 Concentrations', fontsize=16, fontweight='bold')`: Adds a title to the plot, with a larger font size of 16 and bold styling for emphasis.\n",
    "\n",
    "8. **Grid Customization**:\n",
    "   - `plt.grid(True, linestyle=':', color='gray', alpha=0.6)`: Enables a grid for better readability of the plot, using dotted lines (`linestyle=':'`), gray color (`color='gray'`), and a slight transparency (`alpha=0.6`) to keep the grid unobtrusive.\n",
    "\n",
    "9. **X-axis Label Rotation**:\n",
    "   - `plt.xticks(rotation=45, ha='right', fontsize=12)`: Rotates the x-axis labels by 45 degrees for better readability, aligning them to the right (`ha='right'`) with a font size of 12.\n",
    "\n",
    "10. **Y-axis Ticks Customization**:\n",
    "    - `plt.yticks(fontsize=12)`: Sets the font size of the y-axis tick labels to 12 for consistency and readability.\n",
    "\n",
    "11. **Layout Adjustment**:\n",
    "    - `plt.tight_layout()`: Adjusts the layout automatically to ensure that the labels and titles are well-spaced and not cut off.\n",
    "\n",
    "12. **Displaying the Plot**:\n",
    "    - `plt.show()`: Displays the plot with all the modifications and enhancements made above.\n",
    "\n",
    "13. **Elapsed Time Calculation**:\n",
    "    - `end_time = time.time()`: Records the end time after the plot is displayed.\n",
    "    - `elapsed_time = end_time`: Computes the time elapsed between the start and end of the process (though the start time is missing in this snippet).\n",
    "    - `print(f\"Elapsed time: {elapsed_time:.4f} seconds\")`: Prints the elapsed time with a precision of four decimal places.\n",
    "\n",
    "This code generates a visually appealing plot comparing the actual data (training and test sets) with the predicted values from the ARIMA model. By customizing line colors, styles, and labels, the plot is designed to be clear, professional, and suitable for presentations or reports. The grid and label formatting enhance readability, and the overall layout ensures a polished final presentation.\n",
    "\n",
    "Finally, the code in this ARIMA forecasting application is not my own but was adapted and created as a training portfolio project.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}