The uncertainty estimation of feature-based forecast combinations

Abstract

Forecasting is an indispensable element of operational research (OR) and an important aid to planning. The accurate estimation of the forecast uncertainty facilitates several operations management activities, predominantly in supporting decisions in inventory and supply chain management and effectively setting safety stocks. In this paper, we introduce a feature-based framework, which links the relationship between time series features and the interval forecasting performance into providing reliable interval forecasts. We propose an optimal threshold ratio searching algorithm and a new weight determination mechanism for selecting an appropriate subset of models and assigning combination weights for each time series tailored to the observed features. We evaluate our approach using a large set of time series from the M4 competition. Our experiments show that our approach significantly outperforms a wide range of benchmark models, both in terms of point forecasts as well as prediction intervals.

Keywords:

• Forecasting
• time series features
• uncertainty estimation
• forecast combination
• prediction intervals

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

Yanfei Kang is supported by the National Key Research and Development Program (No. 2019YFB1404600) and the National Natural Science Foundation of China (No.11701022). Feng Li is supported by the National Natural Science Foundation of China (No. 11501587) and the Beijing Universities Advanced Disciplines Initiative (No. GJJ2019163). Petropoulos' work was completed during his visit at the Beihang University in April-May 2019. This research was supported by the high-performance computing (HPC) resources at Beihang University.