A WSFA-based adaptive feature extraction method for multivariate time series prediction
Author:
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-09198-1.pdf
Reference48 articles.
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2. Yang C, Qiao J, Wang L, Zhu X (2019) Dynamical regularized echo state network for time series prediction. Neural Comput Appl 31:6781–6794. https://doi.org/10.1007/s00521-018-3488-z
3. Song W, Fujimura S (2021) Capturing combination patterns of long- and short-term dependencies in multivariate time series forecasting. Neurocomputing 464:72–82. https://doi.org/10.1016/j.neucom.2021.08.100
4. Li G, Jung JJ (2023) Deep learning for anomaly detection in multivariate time series: approaches, applications, and challenges. Inf Fusion 91:93–102. https://doi.org/10.1016/j.inffus.2022.10.008
5. Fan J, Zhang K, Huang Y et al (2021) Parallel spatio-temporal attention-based TCN for multivariate time series prediction. Neural Comput Appl. https://doi.org/10.1007/s00521-021-05958-z
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