jvastola / R-TimeSeriesAnalysis-AirPassengers

A time series analysis on the AirPassengers dataset, showcasing decomposition and forecasting using SARIMA.

Time Series Analysis on AirPassengers Dataset

This project showcases a time series analysis on the classic AirPassengers dataset, which represents the monthly totals of international airline passengers from 1949 to 1960.

Objective:

To decompose the time series into its primary components (trend, seasonal, and residual) and forecast future airline passenger numbers using the SARIMA model.

Dataset:

• Name: AirPassengers
• Description: Monthly totals of international airline passengers from 1949 to 1960.
• Source: Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976) Time Series Analysis, Forecasting and Control. Third Edition. Holden-Day. Series G.

Project Structure:

• /data: Contains the dataset in CSV format.
• /scripts: Contains R scripts used for analysis.
  • 01_data_preparation.R: Loads and preprocesses the dataset.
  • 02_time_series_decomposition.R: Decomposes the time series into its components.
  • 03_sarima_model.R: Fits a SARIMA model and forecasts future values.
• /output: Contains generated plots and outputs.

How to Run:

1. Clone the repository to your local machine.
2. Set your working directory in R to the project's root folder.
3. Execute the scripts in the /scripts directory in sequence:
   • 01_data_preparation.R
   • 02_time_series_decomposition.R
   • 03_sarima_model.R
4. Check the /output directory for generated plots.

Results:

The time series was successfully decomposed to showcase the underlying trend, seasonal fluctuations, and residuals. A SARIMA model was fitted to forecast future passenger numbers.

Decomposed Time Series:

SARIMA Forecast: