Statistical Feature-based Search for Multivariate Time Series Forecasting

被引：0
|
作者：
Pan, Jinwei [1 ]
Wang, Yiqiao [1 ]
Zhong, Bo [1 ]
Wang, Xiaoling [1 ]
机构：
[1] School of Computer Science and Technology, East China Normal University, Shanghai
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2024年 / 46卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Attention mechanism; Forecasting; Long-term dependency; Multivariate time series;
D O I：
10.11999/JEIT231264
中图分类号：
学科分类号：
摘要：
There are long-term dependencies, such as trends, seasonality, and periodicity in time series, which may span several months. It is insufficient to apply existing methods in modeling the long-term dependencies of the series explicitly. To address this issue, this paper proposes a Statistical Feature-based Search for multivariate time series Forecasting (SFSF). First, statistical features which include smoothing, variance, and interval standardization are extracted from multivariate time series to enhance the perception of the time series’ trends and periodicity. Next, statistical features are used to search for similar series in historical sequences. The current and historical sequence information is then blended using attention mechanisms to produce accurate prediction results. Experimental results show that the SFSF method outperforms six state-of-the-art methods. © 2024 Science Press. All rights reserved.
引用
收藏
页码：3276 / 3284
页数：8
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