The uncertainty estimation of feature-based forecast combinations

Xiaoqian Wang, Yanfei Kang, Fotios Petropoulos, Feng Li

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
JournalJournal of the Operational Research Society
Early online date8 Mar 2021
DOIs
Publication statusE-pub ahead of print - 8 Mar 2021

Fingerprint

Dive into the research topics of 'The uncertainty estimation of feature-based forecast combinations'. Together they form a unique fingerprint.

Cite this