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.
To decompose the time series into its primary components (trend, seasonal, and residual) and forecast future airline passenger numbers using the SARIMA model.
- 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.
/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.
- Clone the repository to your local machine.
- Set your working directory in R to the project's root folder.
- Execute the scripts in the
/scripts
directory in sequence:01_data_preparation.R
02_time_series_decomposition.R
03_sarima_model.R
- Check the
/output
directory for generated plots.
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.