Time-series forecasting: an empirical evaluation of the state of the art, ensembles- and meta-learning strategies

Abstract

Techniques of hybridisation and ensemble learning have been popular for improving the predictive power of forecasting methods during the past two decades. With limited research that combines these two promising approaches, this study focuses on the utility of the Exponential-Smoothing Recurrent Neural Network (ES-RNN) in the pool of base models for different ensembles. This study compares some state of the art ensemble techniques and arithmetic model averaging as a benchmark. This study experiments with the M4 forecasting competition’s data set of 100, 000 time-series, and the results show that the Feature-based Forecast Model Averaging (FFORMA), on average, is the best technique for late data fusion with the ES-RNN. However, considering the M4’s Daily subset of data, stacking was the only successful ensemble at dealing with the case where all base model performances are similar. The experimental results conclude that model averaging is a more robust ensemble than model selection and stacking strategies. Further, the results show that gradient boosting is superior for implementing ensemble learning strategies.