https://github.com/sabin74/arima_time_series_forecasting

This project demonstrates time series forecasting using AR, ARIMA, and SARIMA models to predict monthly airline passenger counts from 1949 to 1960. The project follows a complete pipeline including data preprocessing, stationarity checks, model fitting, diagnostics, and future forecasting using Python's statsmodels library.
https://github.com/sabin74/arima_time_series_forecasting

arima arima-forecasting kaggle-dataset pandas python sarima statsmodels time-series-forecasting

Last synced: 3 months ago
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This project demonstrates time series forecasting using AR, ARIMA, and SARIMA models to predict monthly airline passenger counts from 1949 to 1960. The project follows a complete pipeline including data preprocessing, stationarity checks, model fitting, diagnostics, and future forecasting using Python's statsmodels library.

• Host: GitHub
• URL: https://github.com/sabin74/arima_time_series_forecasting
• Owner: sabin74
• Created: 2025-06-21T11:59:38.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-06-21T12:19:03.000Z (about 1 year ago)
• Last Synced: 2025-06-21T13:28:20.415Z (about 1 year ago)
• Topics: arima, arima-forecasting, kaggle-dataset, pandas, python, sarima, statsmodels, time-series-forecasting
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/datasets/rakannimer/air-passengers/data
• Size: 3.59 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# ✈️ ARIMA Time Series Forecasting

This project demonstrates time series forecasting using **AR**, **ARIMA**, and **SARIMA** models to predict monthly airline passenger counts from 1949 to 1960. The project follows a complete pipeline including data preprocessing, stationarity checks, model fitting, diagnostics, and future forecasting using Python's `statsmodels` library.

## 📁 Dataset

- **Name**: Airline Passengers Dataset

- **Source**: Monthly total of international airline passengers (1949–1960)

- **Format**: CSV with columns: `Month`, `Passengers`

## 🧰 Tools & Libraries Used

- Python

- pandas

- matplotlib, seaborn

- statsmodels (for AR, ARIMA, SARIMA models)

- sklearn.metrics (for evaluation)

## 🚦 Project Workflow

 1. Data Preprocessing

 2. Exploratory Data Analysis (EDA)

 3. ACF & PACF

 4. AR and ARIMA Modeling

 5. SARIMA Modeling

 6. Forecasting & Evaluation