Forecasting with a large number of predictors: A tree based moving average approach

A large number of predictors brings valuable information to time series forecasting problems, as well as difficulty in capturing this information. Short-term electricity load forecasting (STLF) problems are examples of such problems with many predictors, including several temperature values from different regions, a large number of special days, and multiple time-related variables. In such cases, forecasting with many predictors can be problematic in terms of robustness to redundant predictors, tendency to overfit, and the curse of dimensionality. To resolve these problems, this study proposes a novel tree-based ensemble model, tree-based moving average (TBMA), that provides point and probabilistic forecasts and works as an automated feature extraction method. The proposed model deals with a large number of predictors without sacrificing accuracy and does not require a complicated parameter tuning process as the advantages of being a tree-based ensemble model. The distinctive feature of the proposed model from existing tree-based ensemble models is that the suggested model considers the autocorrelation in time series data with the integrated moving average model and extracts useful features. Our comprehensive experiments show that boosting approaches provide significantly better results when features from TBMA are introduced. The proposed approach also provides competitive results compared to benchmark models in point and probabilistic forecasting of Turkey's electricity load.