Time Series Analysis

Project Work & Implementation in R, Python

This repository contains my project work for the Time Series Analysis course.
The project focuses on analyzing a univariate time series, selecting an appropriate ARIMA model, performing diagnostics, and generating forecasts using R.

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Topics Covered

• Trend and seasonality modeling
• Stationarity checks (ACF, PACF, differencing)
• Unit root testing (ADF)
• ARIMA model selection (AIC/BIC comparison)
• Residual diagnostics (Ljung–Box, normality tests)
• Forecasting with confidence intervals
• Model evaluation (MSE)

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Repository Structure

The repository includes:

• Clean and well‑commented R code, Python code
• Diagnostic plots and forecast visualizations in the PDF
• Model comparison tables and evaluation metrics in the PDF

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Technologies Used

• R
• RStudio
• forecast, tseries, ggplot2, and other time‑series packages
• Python

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Notes

• This repository contains only my own project implementation and analysis.
• No confidential course materials or restricted content are included.
• All datasets used are those provided for the assignment or publicly accessible.