Long sequence time-series forecasting with deep learning: A survey

Abstract

The development of deep learning technology has brought great improvements to the field of time series forecasting. Short sequence time-series forecasting no longer satisfies the current research community, and long-term future prediction is becoming the hotspot, which is noted as long sequence time-series forecasting (LSTF). The LSTF has been widely studied in the extant literature, but few reviews of its research development are reported. In this article, we provide a comprehensive survey of LSTF studies with deep learning technology. We propose rigorous definitions of LSTF and summarize the evolution in terms of a proposed taxonomy based on network structure. Next, we discuss three key problems and corresponding solutions from long dependency modeling, computation cost, and evaluation metrics. In particular, we propose a Kruskal–Wallis test based evaluation method for evaluation metrics problems. We further synthesize the applications, datasets, and open-source codes of LSTF. Moreover, we conduct extensive case studies comparing the proposed Kruskal–Wallis test based evaluation method with existing metrics and the results demonstrate the effectiveness. Finally, we propose potential research directions in this rapidly growing field. All resources and codes are assembled and organized under a unified framework that is available online at https://github.com/Masterleia/TSF_LSTF_Compare.