{"id":28907616,"url":"https://github.com/sabin74/arima_time_series_forecasting","last_synced_at":"2026-05-09T09:12:36.697Z","repository":{"id":300395070,"uuid":"1006059746","full_name":"sabin74/ARIMA_time_series_forecasting","owner":"sabin74","description":"This project demonstrates time series forecasting using AR, ARIMA, and SARIMA models to predict monthly airline passenger counts from 1949 to 1960. 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The project follows a complete pipeline including data preprocessing, stationarity checks, model fitting, diagnostics, and future forecasting using Python's `statsmodels` library.\n\n## 📁 Dataset\n\n- **Name**: Airline Passengers Dataset\n- **Source**: Monthly total of international airline passengers (1949–1960)\n- **Format**: CSV with columns: `Month`, `Passengers`\n\n\n## 🧰 Tools \u0026 Libraries Used\n\n- Python\n- pandas\n- matplotlib, seaborn\n- statsmodels (for AR, ARIMA, SARIMA models)\n- sklearn.metrics (for evaluation)\n\n\n## 🚦 Project Workflow\n 1. Data Preprocessing\n 2. Exploratory Data Analysis (EDA)\n 3. ACF \u0026 PACF\n 4. AR and ARIMA Modeling\n 5. SARIMA Modeling\n 6. 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