Combining Multiple Time Series Models Through A Robust Weighted Mechanism

TL;DR

This paper introduces a robust nonlinear ensemble method for combining multiple time series models that accounts for correlations among models, leading to significantly improved forecasting accuracy over traditional linear methods.

Contribution

The paper proposes a novel weighted nonlinear ensemble technique that considers model correlations, enhancing forecast accuracy beyond existing linear combination approaches.

Findings

The proposed ensemble outperforms individual models in forecast accuracy.

It achieves significantly lower forecast errors than linear combination methods.

The method is validated on three real-world time series datasets.

Abstract

Improvement of time series forecasting accuracy through combining multiple models is an important as well as a dynamic area of research. As a result, various forecasts combination methods have been developed in literature. However, most of them are based on simple linear ensemble strategies and hence ignore the possible relationships between two or more participating models. In this paper, we propose a robust weighted nonlinear ensemble technique which considers the individual forecasts from different models as well as the correlations among them while combining. The proposed ensemble is constructed using three well-known forecasting models and is tested for three real-world time series. A comparison is made among the proposed scheme and three other widely used linear combination methods, in terms of the obtained forecast errors. This comparison shows that our ensemble scheme provides significantly lower forecast errors than each individual model as well as each of the four linear combination methods.