Statistical Feature-based Search for Multivariate Time Series Forecasting

PAN Jinwei; WANG Yiqiao; ZHONG Bo; WANG Xiaoling

doi:10.11999/JEIT231264

Volume 46 Issue 8

Aug. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(8): 3276-3284

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PAN Jinwei, WANG Yiqiao, ZHONG Bo, WANG Xiaoling. Statistical Feature-based Search for Multivariate Time Series Forecasting[J]. Journal of Electronics & Information Technology, 2024, 46(8): 3276-3284. doi: 10.11999/JEIT231264

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Statistical Feature-based Search for Multivariate Time Series Forecasting

doi: 10.11999/JEIT231264 cstr: 32379.14.JEIT231264

School of Computer Science and Technology, East China Normal University, Shanghai 200062, China

Funds: The National Natural Science Foundation of China (61972155)

Received Date: 2023-11-15
Rev Recd Date: 2024-07-14

Available Online: 2024-07-29

Publish Date: 2024-08-30

Abstract

Abstract

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.
- Multivariate time series,
- Forecasting,
- Attention mechanism,
- Long-term dependency

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References(17)

References

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