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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2020

30.07.2020

Time Series Prediction Method Based on Variant LSTM Recurrent Neural Network

verfasst von: Jiaojiao Hu, Xiaofeng Wang, Ying Zhang, Depeng Zhang, Meng Zhang, Jianru Xue

Erschienen in: Neural Processing Letters | Ausgabe 2/2020

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Abstract

Time series prediction problems are a difficult type of predictive modeling problem. In this paper, we propose a time series prediction method based on a variant long short-term memory (LSTM) recurrent neural network. In the proposed method, we firstly improve the memory module of the LSTM recurrent neural network by merging its forget gate and input gate into one update gate, and using Sigmoid layer to control information update. Using improved LSTM recurrent neural network, we develop a time series prediction model. In the proposed model, the parameter migration method is used model update to ensure the model has good predictive ability after predicting multi-step sequences. Experimental results show, compared with several typical time series prediction models, the proposed method have better performance for long-sequence data prediction.
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Fußnoten
1
The dataset 1 and dataset 2 used in the experiment are come from the specific application.
 
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Metadaten
Titel
Time Series Prediction Method Based on Variant LSTM Recurrent Neural Network
verfasst von
Jiaojiao Hu
Xiaofeng Wang
Ying Zhang
Depeng Zhang
Meng Zhang
Jianru Xue
Publikationsdatum
30.07.2020
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-020-10319-3

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