Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11063-020-10319-3 | NEURAL PROCESSING LETTERS |
Keywords | DocType | Volume |
Deep learning,Time series prediction,Recurrent neural network,Variant LSTM network | Journal | 52.0 |
Issue | ISSN | Citations |
SP2.0 | 1370-4621 | 2 |
PageRank | References | Authors |
0.37 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiaojiao Hu | 1 | 2 | 0.37 |
| Xiaofeng Wang | 2 | 94 | 9.88 |
| Ying Zhang | 3 | 163 | 25.25 |
| Depeng Zhang | 4 | 2 | 0.37 |
| Meng Zhang | 5 | 2 | 0.37 |
| J. Xue | 6 | 542 | 57.57 |