Enhanced regression modeling using machine learning methods with autoregressive errors

Regression with autoregressive errors is a critical area in time series analysis. This article develops a hybrid modeling framework combining classical regression with machine learning methods to explicitly handle autoregressive error structures. We investigate the integration of autoregressive components into penalized and tree-based models to capture both linearity and nonlinearity in predictors and temporal dependence in residuals. The performances of all these models are compared with one another using simulated and real data example.