This project demonstrates the application of advanced time series modeling techniques, specifically ARIMA and VAR, using R. It involves forecasting and trend analysis on real-world datasets, focusing on the implementation of robust statistical models to derive actionable insights.
siddhantj12-Time-Series-Modeling-in-R-ARIMA-VAR-Models/
├── ARIMA FINAL.r # Script for ARIMA model implementation and analysis
├── ESS data.csv # Dataset used for modeling and analysis
└── VARS FINAL.r # Script for VAR model implementation and analysis
- ARIMA Modeling: Forecasting univariate time series data by identifying optimal parameters and assessing model performance.
- VAR Modeling: Analyzing multivariate time series relationships and interdependencies for robust forecasting.
- Data Analysis: Preprocessing and visualizing data to extract meaningful trends and patterns.
- R Programming: Used for statistical computing and modeling.
- ARIMA and VAR Libraries: Key libraries in R for time series forecasting and analysis.
- ESS Dataset: Dataset used to train and validate the models.
- Clone the repository to your local machine.
- Open the
ARIMA FINAL.rorVARS FINAL.rscripts in RStudio or your preferred R environment. - Ensure the
ESS data.csvfile is in the same directory. - Execute the scripts to perform time series analysis and view results.
- ARIMA Model: Achieved a significant improvement in forecast accuracy through parameter optimization.
- VAR Model: Successfully analyzed interdependent variables, revealing key insights into multivariate time series dynamics.
- Integration of more advanced forecasting techniques such as Prophet or machine learning models.
- Application of the models to other datasets for broader insights.
Special thanks to the open-source R community for providing robust libraries for time series analysis.