Machine Learning-Based Forecasting of Multivariate Time Series: Evidence from Random Forest and Extreme Gradient Boosting for VAR Model

Conventional Vector Autoregressive (VAR) models are widely applied for multivariate time series analysis, their performance deteriorates in high-dimensional settings due to inefficient parameter estimation, unstable forecasts, and difficulties in interpreting temporal dependencies. This study conducts a comparative study on conventional Vector Autoregressive (VAR) models, Multivariate Random Forest for VAR (MRF-VAR) models and Multivariate Extreme Gradient Boosting for VAR (MXGB-VAR) models, validated using simulated and real-life dataset for Nigerian financial time series. Augmented Dickey–Fuller (ADF) test was adopted to test the stationarity of the data. Forecast accuracy across short-term and long-term horizons for the models were measured using Mean Absolute Deviation (MAD) and Root Mean Square Deviation (RMSD). Results for simulated data show that the conventional VAR models achieved the best short-term forecast performance (MAD = 1.642, RMSD = 2.016), while the MRF-VAR models performed best in long-term forecasting (MAD = 0.947, RMSD = 1.197). In the case of the real-life dataset for Nigerian financial time series, the MRF-VAR model performed well in short-term forecasts (MAD = 108.84, RMSD = 149.53), whereas MXGB-VAR model provided better results in long-term forecasts (RMSD = 730.57). Policymakers and financial analysts should be encouraged to apply machine learning approaches to VAR models in macroeconomic and financial forecasting to improve decision-making.