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SVSeq2Seq: An Efficient Computational Method for State Vectors in Sequence-to-Sequence Architecture Forecasting

Author

Listed:
  • Guoqiang Sun

    (School of Information Science and Engineering, Shandong University, Qingdao 266237, China)

  • Xiaoyan Qi

    (State Key Laboratory of Microbial Technology, Shandong University, Qingdao 266237, China)

  • Qiang Zhao

    (School of Information Science and Engineering, Shandong University, Qingdao 266237, China)

  • Wei Wang

    (School of Information Science and Engineering, Shandong University, Qingdao 266237, China)

  • Yujun Li

    (School of Information Science and Engineering, Shandong University, Qingdao 266237, China)

Abstract

This study proposes an efficient method for computing State Vectors in Sequence-to-Sequence (SVSeq2Seq) architecture to improve the performance of sequence data forecasting, which associates each element with other elements instead of relying only on nearby elements. First, the dependency between two elements is adaptively captured by calculating the relative importance between hidden layers. Second, tensor train decomposition is used to address the issue of dimensionality catastrophe. Third, we further select seven instantiated baseline models for data prediction and compare them with our proposed model on six real-world datasets. The results show that the Mean Square Error (MSE) and Mean Absolute Error (MAE) of our SVSeq2Seq model exhibit significant advantages over the other seven baseline models in predicting the three datasets, i.e., weather, electricity, and PEMS, with MSE/MAE values as low as 0.259/0.260, 0.186/0.285 and 0.113/0.222, respectively. Furthermore, the ablation study demonstrates that the SVSeq2Seq model possesses distinct advantages in sequential forecasting tasks. It is observed that replacing SVSeq2Seq with LPRcode and NMTcode resulted in an increase under an MSE of 18.05 and 10.11 times, and an increase under an MAE of 16.54 and 9.8 times, respectively. In comparative experiments with support vector machines (SVM) and random forest (RF), the performance of the SVSeq2Seq model is improved by 56.88 times in the weather dataset and 73.78 times in the electricity dataset under the MSE metric, respectively. The above experimental results demonstrate both the exceptional rationality and versatility of the SVSeq2Seq model for data forecasting.

Suggested Citation

  • Guoqiang Sun & Xiaoyan Qi & Qiang Zhao & Wei Wang & Yujun Li, 2024. "SVSeq2Seq: An Efficient Computational Method for State Vectors in Sequence-to-Sequence Architecture Forecasting," Mathematics, MDPI, vol. 12(2), pages 1-17, January.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:2:p:265-:d:1318584
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    References listed on IDEAS

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    1. Sneha Jadhav & Jianxiang Zhao & Yepeng Fan & Jingjing Li & Hao Lin & Chenggang Yan & Minghan Chen, 2023. "Time-Varying Sequence Model," Mathematics, MDPI, vol. 11(2), pages 1-15, January.
    2. Jiaping Cao & Jichao Li & Jiang Jiang, 2023. "Link Prediction for Temporal Heterogeneous Networks Based on the Information Lifecycle," Mathematics, MDPI, vol. 11(16), pages 1-17, August.
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