short-paper

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

Authors:

Hao Li,

Jie Shao,

Kewen Liao,

Mingjian TangAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 4168 - 4172

https://doi.org/10.1145/3511808.3557585

Published: 17 October 2022 Publication History

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Abstract

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.

Supplementary Material

MP4 File (CIKM2022-sp0417.mp4)

This is a presentation video for the short paper sp0417. In this presentation, we give an introduction to the background, related works, and challenges of long sequence time series forecasting(LSTF). We introduce how to use two statistical methods to address the challenges of long sequence time series forecasting. We show the performance of these two methods and give some advice to do more research on LSTF.

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Index Terms

Do Simpler Statistical Methods Perform Better in Multivariate Long Sequence Time-Series Forecasting?
1. Mathematics of computing
  1. Probability and statistics

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CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 17 October 2022

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October 17 - 21, 2022

GA, Atlanta, USA

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Zhou SAnastasiu DSerra ESpezzano F(2024)Long-Term Hydrologic Time Series Prediction with LSPMProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679957(4308-4312)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679957
Truchan HKalfar CAhmadi ZSerra ESpezzano F(2024)LTBoost: Boosted Hybrids of Ensemble Linear and Gradient Algorithms for the Long-term Time Series ForecastingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679527(2271-2281)Online publication date: 21-Oct-2024
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