Hybrid time series prediction model based on SSA-VMD-TCN

Fan Wei; Zhonglin Zhang; Haiyun Ma

doi:10.1117/12.2639703

19 October 2022 Hybrid time series prediction model based on SSA-VMD-TCN

Fan Wei, Zhonglin Zhang, Haiyun Ma

Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122945O (2022) https://doi.org/10.1117/12.2639703
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China

Abstract

In order to improve the prediction accuracy of non-stationary time series, this paper proposes a deep learning hybrid model SSA-VMD-TCN based on sparrow search algorithm (SSA), variational mode decomposition (VMD) and sequential convolution network (TCN). The model achieves better prediction effect by reducing the complexity of nonlinear sequence. The sSA-VMD-TCN model first uses VMD to effectively decompose the original sequence into a certain number of intrinsic modal components (IMF) and residual components. Meanwhile, SSA algorithm is used to optimize the input parameters ofTCN prediction model, and then the models are modeled on each IMF. Finally, the results of each sequence test set are added as the final result. This shows that the model is an effective time series forecasting model.

Citation Download Citation

Fan Wei, Zhonglin Zhang, and Haiyun Ma "Hybrid time series prediction model based on SSA-VMD-TCN", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122945O (19 October 2022); https://doi.org/10.1117/12.2639703

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