Proceedings of the 2nd International Conference on Intelligent Design and Innovative Technology (ICIDIT 2023)

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Prediction Study Based on TCN-BiLSTM-SA Time Series Model

Authors
He Zhang1, *, Peng Chu2
1School of Electronic Information, Xijing University, Xi’an, China
2School of Electronic Information, Xijing University, Xi’an, China
*Corresponding author. Email: Z18437379496@163.com
Corresponding Author
He Zhang
Available Online 10 October 2023.
DOI
10.2991/978-94-6463-266-8_21How to use a DOI?
Keywords
Time series prediction; TCN; BiLSTM; Self-Attention
Abstract

To enhance the accuracy of time series prediction, this study proposes a hybrid network model called TCN-BiLSTM-SA, which combines Temporal Convolutional Network (TCN), Bidirectional Long Short-term Memory (BiLSTM), and Self Attention (SA). The TCN is employed to learn sequence features, while the BiLSTM model captures preceding and succeeding states to extract more information for prediction. The self-attention mechanism calculates weights for each time step's output, effectively utilizing the cell memory information of BiLSTM to capture global features and improve prediction accuracy. Experimental results on the Beijing PM 2.5 dataset demonstrate that the TCN-BiLSTM-SA network outperforms the BiLSTM model in terms of RMSE, MAE, and MAPE error rates, while also exhibiting greater stability. This model holds promising potential for various time series prediction applications. It has broad application prospects in time series prediction.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2nd International Conference on Intelligent Design and Innovative Technology (ICIDIT 2023)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
10 October 2023
ISBN
978-94-6463-266-8
ISSN
2589-4919
DOI
10.2991/978-94-6463-266-8_21How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - He Zhang
AU  - Peng Chu
PY  - 2023
DA  - 2023/10/10
TI  - Prediction Study Based on TCN-BiLSTM-SA Time Series Model
BT  - Proceedings of the 2nd International Conference on Intelligent Design and Innovative Technology (ICIDIT 2023)
PB  - Atlantis Press
SP  - 192
EP  - 197
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-266-8_21
DO  - 10.2991/978-94-6463-266-8_21
ID  - Zhang2023
ER  -