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An integrated approach implementing sliding window and DTW distance for time series forecasting tasks

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Abstract

To handle the scenario of time delay in single predicted results, a novel time-variant weighting method by integrating dynamic time warping (DTW) distance and sliding window model is introduced in combination forecasting. The usage of sliding window can model micro-level statistical characteristics contained in the observed time series and the predicted time series. While the introduction of DTW algorithm can not only measure the distance between the observed time series and the predicted time series, but also the similarity between the two sequences. The integration of sliding window and DTW distance could provide a novel mechanism that can comprehensively reflect the total and partial statistical characteristics of time series in combination forecasting. Two numerical studies show the feasibility and validity of the developed combination forecasting model.

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Acknowledgements

The authors would like to thank the editors and the anonymous reviewers for their valuable comments and suggestions, which have helped immensely in improving the quality of this paper.

Funding

The work was supported in part by the Humanities and Social Sciences Research Youth Project of the Ministry of Education of China (No. 21YJCZH148), the Natural Science Foundation of Anhui Province (Nos. 2108085MG239, 2108085QG290, 2008085QG334, 2008085MG226), the National Natural Science Foundation of China under Grants (Nos. 71901088, 72171002, 72001001, 72071001) and The teacher project of Anhui Ecology and Economic Development Research Center in 2021 (No. AHST2021002).

Author information

Authors and Affiliations

  1. School of Big Data and Statistics, Anhui University, Hefei, Anhui, 230601, China

    Zhifu Tao & Qinghua Xu

  2. Research Center for Finance and Statistics, Anhui University, Hefei, Anhui, 230601, China

    Zhifu Tao & Qinghua Xu

  3. Research Center for Applied Mathematics Research, Anhui University, Hefei, Anhui, 230601, China

    Zhifu Tao & Qinghua Xu

  4. School of Mathematics and Statistics, Hefei Normal University, Hefei, Anhui, 230601, China

    Xi Liu

  5. School of Business, Anhui University, Hefei, Anhui, 230601, China

    Jinpei Liu

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  1. Zhifu Tao
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  2. Qinghua Xu
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Correspondence to Xi Liu.

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This manuscript has not been published in whole or in part elsewhere and is not currently being considered for publication in another journal. All authors have been personally and actively involved in substantive work leading to the manuscript and will hold themselves jointly and individually responsible for its content. The authors declare that there are no conflicts of interest.

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Tao, Z., Xu, Q., Liu, X. et al. An integrated approach implementing sliding window and DTW distance for time series forecasting tasks. Appl Intell 53, 20614–20625 (2023). https://doi.org/10.1007/s10489-023-04590-9

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