Long Time Series Deep Forecasting with Multiscale Feature Extraction and Seq2seq Attention Mechanism-Reference-Cited by-同舟云学术

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Long Time Series Deep Forecasting with Multiscale Feature Extraction and Seq2seq Attention Mechanism

Published:2022-03-09 Issue:4 Volume:54 Page:3443-3466
ISSN:1370-4621
Container-title:Neural Processing Letters
language:en
Short-container-title:Neural Process Lett

Author:

Wang Xin,Cai Zhiming,Luo Yixian,Wen Zhijie^ORCID,Ying Shihui

Funder

the national natural foundation of china

the capacity construction project of local universities in shanghai under grant

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Link

https://link.springer.com/content/pdf/10.1007/s11063-022-10774-0.pdf

Reference33 articles.

1. Sagheer A, Kotb M (2019) Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing 323:203–213. https://doi.org/10.1016/j.neucom.2018.09.082

2. Liu J, Wang X, Zhao Y, Dong B, Lu K, Wang R (2020) Heating load forecasting for combined heat and power plants via strand-based LSTM. IEEE Access 8:33360–33369. https://doi.org/10.1109/ACCESS.2020.2972303

3. Parras-Gutierrez E, Rivas VM, Garcia-Arenas M, del Jesus MJ (2014) Short, medium and long term forecasting of time series using the L-Co-Ralgorithm. Neurocomputing 128:433–446. https://doi.org/10.1016/j.neucom.2013.08.023

4. Yang HM, Pan ZS, Bai W (2019) Review of time series prediction methods. Comput Sci 46(1):21–28. https://doi.org/10.11896/j.issn.1002-137X.2019.01.004

5. Lukoseviciute K, Baubliene R, Howard D, Ragulskis M (2018) Bernstein polynomials for adaptive evolutionary prediction of short-term time series. Appl Soft Comput 65:47–57. https://doi.org/10.1016/j.asoc.2018.01.002

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