CDA-LSTM: an evolutionary convolution-based dual-attention LSTM for univariate time series prediction
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-021-06212-2.pdf
Reference42 articles.
1. Song G, Dai Q (2017) A novel double deep ELMs ensemble system for time series forecasting. Knowl-Based Syst 134:31–49. https://doi.org/10.1016/j.knosys.2017.07.014
2. Längkvist M, Karlsson L, Loutfi A (2014) A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recogn Lett 42:11–24. https://doi.org/10.1016/j.patrec.2014.01.008
3. Yang H-F, Chen Y-PP (2019) Hybrid deep learning and empirical mode decomposition model for time series applications. Expert Syst Appl 120:128–138. https://doi.org/10.1016/j.eswa.2018.11.019
4. Kanarachos S, Christopoulos S-RG, Chroneos A, Fitzpatrick ME (2017) Detecting anomalies in time series data via a deep learning algorithm combining wavelets, neural networks and Hilbert transform. Expert Syst Appl 85:292–304. https://doi.org/10.1016/j.eswa.2017.04.028
5. Rodrigues F, Markou I, Pereira FC (2019) Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach. Inf Fusion 49:120–129. https://doi.org/10.1016/j.inffus.2018.07.007
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