A New MC-LSTM Network Structure Designed for Regression Prediction of Time Series-Reference-Cited by-同舟云学术

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A New MC-LSTM Network Structure Designed for Regression Prediction of Time Series

Published:2023-03-02 Issue:7 Volume:55 Page:8957-8979
ISSN:1370-4621
Container-title:Neural Processing Letters
language:en
Short-container-title:Neural Process Lett

Author:

Yang Haifeng,Hu Juanjuan,Cai Jianghui^ORCID,Wang Yupeng,Chen Xin,Zhao Xujun,Wang Lili

Funder

the National Natural Science Foundation of China

Key Research and Development Projects of Shanxi Province

the central government guides local science and technology development funds

the Fundamental Research Program of Shanxi Province

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/s11063-023-11187-3.pdf

Reference49 articles.

1. Gers FA, Eck D, Schmidhuber J (2002) Applying LSTM to time series predictable through time-window approaches. In: Artificial Neural Networks—ICANN 2001: International Conference Vienna, Austria, August 21–25, 2001 Proceedings, vol 11, pp 669–676

2. Qu CX, Yang HF, Cai JH, Xun YL (2020) P-cygni profile analysis of the spectrum: Lamost j152238.11+333136.1. Spectrosc Spect Anal 40:1304

3. Tukymbekov D, Saymbetov A, Nurgaliyev M, Kuttybay N, Dosymbetova G, Svanbayev Y (2021) Intelligent autonomous street lighting system based on weather forecast using lstm. Energy 231:120902

4. Xu Y, Lu X, Cetiner B, Taciroglu E (2020) Real time regional seismic damage assessment framework based on long short term memory neural network. Comput Aided Civ Infrast Eng 36(4):504–521

5. Mikolov T, Karafiát M, Burget L, Cernocký J, Khudanpur S (2010) Recurrent neural network based language model. Interspeech 2(3):1045–1048

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