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Do Simpler Statistical Methods Perform Better in Multivariate Long Sequence Time-Series Forecasting? by Hao Li; Jie Shao; Kewen Liao; Mingjian Tang is a scholarly article available to read on EtoBox.

What is Do Simpler Statistical Methods Perform Better in Multivariate Long Sequence Time-Series Forecasting? about?

Long sequence time-series forecasting has become a central problem in multivariate time-series analysis due to its difficulty of consistently maintaining low prediction errors. Recent research has concentrated on developing large deep learning frameworks such as Informer and SCINet with remarkable results. However, these complex approaches were not benchmarked with simpler statistical methods and hence this part of the puzzle is missing for multivariate long sequence time-series forecasting (MLSTF). We investigate two simple statistical methods for MLSTF and provide analysis to indicate that linear regression owns a lower upper bound of error than deep learning methods and SNaive can act as an effective nonparametric method with unpredictable trends. Evaluations across six real-world datasets demonstrate that linear regression and SNaive are able to achieve state-of-the-art performance for MLSTF. ## CCS CONCEPTS • Mathematics of computing → Probability and statistics.

Author

Hao Li; Jie Shao; Kewen Liao; Mingjian Tang

Publisher

ACM

Published

2022

Language

EN