Add Time Series Analysis and ARIMA Modeling Implementation in R

[iampratik13]

This PR introduces a detailed and educational implementation of Time Series Analysis and ARIMA Modeling in R.

Overview

The TimeSeriesAnalyzer R6 class provides a complete framework for time series modeling, from data preparation and stationarity testing to ARIMA model fitting, forecasting, and diagnostics.

Features

• Augmented Dickey-Fuller (ADF) test for stationarity
• Automatic ACF and PACF computation
• Manual ARIMA model fitting with AIC-based model selection
• Forecast generation with confidence intervals
• Model diagnostics including:
  • Ljung–Box test (autocorrelation)
  • Jarque–Bera normality test
  • ARCH effect test (heteroskedasticity)

Demonstration

Run demonstrate_time_series_analysis() to:

• Generate a synthetic AR(1) process with trend and seasonality
• Perform stationarity testing
• Compute ACF/PACF
• Select best ARIMA(p,d,q) model
• Generate 12-step-ahead forecasts with 95% confidence intervals
• Perform full residual diagnostics

Complexity

• ADF Test: O(n)
• ACF/PACF: O(n × lag)
• ARIMA Fitting (grid search): O(p × d × q × n)

This addition strengthens the R statistical modeling module, offering a powerful, self-contained tool for time series forecasting and model evaluation.

[Copilot]

Pull Request Overview

This PR introduces a comprehensive time series analysis and ARIMA modeling implementation in R, featuring an R6 class-based approach for educational and practical statistical modeling.

Key Changes:

• Implements TimeSeriesAnalyzer R6 class with complete ARIMA workflow
• Provides stationarity testing, model selection, forecasting, and diagnostic capabilities
• Includes demonstration function showcasing synthetic time series analysis

[Copilot]

Pull Request Overview

Copilot reviewed 1 out of 1 changed files in this pull request and generated 7 comments.

[Copilot]

Pull Request Overview

Copilot reviewed 1 out of 1 changed files in this pull request and generated 5 comments.