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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Notes
The dataset 1 and dataset 2 used in the experiment are come from the specific application.
References
Priyamvada, Wadhvani R (2017) Review on various models for time series forecasting. In: Proceedings of international conference on inventive computing and informatics, Coimbatore, India, 23–24 Nov, pp 405–410
Zhou M, Han T (2016) A model of oil price forecasting based on autoregressive and moving average. In: Proceedings of international conference on robots and intelligent system, Zhangjiajie, China, 27–28 Aug, pp 22–25
Ge M, Kerrigan EC (2016) Short-term ocean wave forecasting using an autoregressive moving average model. In: Proceedings of UKACC international conference on control, Belfast, UK, 31 Aug–2 Sept, pp 1–6
Ho SL, Xie M (1998) The use of ARIMA models for reliability forecasting and analysis. Comput Ind Eng 35(1–2):213–216
Vapnik V (1996) The nature of statistical learning theory. Technometrics 38(4):409
Hecht-Nielsen R (1989) Theory of the backpropagation neural network. In: Proceedings of international 1989 joint conference on neural networks, Washington, DC, USA, pp 593–605
Li P, Tan Z, Lili Y et al (2011) Time series prediction of mining subsidence based on a SVM. Int J Min Sci Technol 21(4):557–562
Wang Z, Zhang M, Wang D et al (2017) Failure prediction using machine learning and time series in optical network. Opt Express 25(16):18553
Crone S, Kourentzes N (2010) Feature selection for time series prediction a combined filter and wrapper approach for neural networks. Neurocomputing 73:1923–1936
Boné R, Assaad M, Crucianu M (2013) Boosting recurrent neural networks for time series prediction. In: Pearson DW, Steele NC, Albrecht RF (eds) Artificial neural nets and genetic algorithms. Springer, Vienna
Wang Y (2019) Prediction of PM2.5 concentration in Chengdu based on optimized BP neural network. In: 2019 7th international conference on machinery, materials and computing technology, Chongqing, China, 30–31 May, pp 103–108
Chen Y, Zhao Y, Yan P (2016) Daily ETC traffic flow time series prediction based on k-NN and BP neural network. In: 2nd international conference of young computer scientists, engineers and educators, Harbin, China, 20–22 Aug, pp 135–146
Li L, Jiang P, Xu H, Lin G, Wu H (2019) Water quality prediction based on recurrent neural network and improved evidence theory: a case study of Qiantang River, China. Environ Sci Pollut Res 26(4):19879–19896
Azzouni A, Pujolle G (2017) A long short-term memory recurrent neural network framework for network traffic matrix prediction, pp 1–7
Panapongpakorn T, Banjerdpongchai D (2019) Short-term load forecast for energy management systems using time series analysis and neural network method with average true range. In: 2019 first international symposium on instrumentation, control, artificial intelligence, and robotics (ICA-SYMP), Shanghai, China, 29–31 May, pp 86–89
Wang X, Wu J, Liu C, Yang H, Du Y, Niu W (2018) Fault time series prediction based on LSTM recurrent neural network. J Beijing Univ Aeronaut Astron 44(4):772–784
Zhanga J, Zhub Y, Zhanga X, Yec M, Yangb J (2018) Developing a long short-term memory model for predicting water table depth in agricultural areas. J Hydrol 561:918–929
Fu R, Zhang Z, Li L (2016) Using LSTM and GRU neural network methods for traffic flow prediction. In: 2016 31st youth academic annual conference of Chinese Association of Automation (YAC), Wuhan, China, 11–13 Nov
Yuan X, Li L, Wang Y (2019) Nonlinear dynamic soft sensor modeling with supervised long short-term memory network. IEEE Trans Ind Inform 16:3168–3176
Tomasz P (2015) Using evolutionary neural networks to predict spatial orientation of a ship. Neurocomputing 166:229–243
Kingma D P, Ba J (2014) Adam: a method for stochastic optimization. In: LCLR, pp 1–15
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work was supported by the National Natural Science Foundation of China under Grant No. 61772416; the National Major Research and Development Plan Program of China under Grant No. 2016YFB1001004; the Key Laboratory Project of the Education Department of Shaanxi Province under Grant No. 17JS098; Thirteenth Five-Year Equipment Pre-research Project No. 30503030201-02; the foundation of the State Key Laboratory of Astronautic Dynamics.
Rights and permissions
About this article
Cite this article
Hu, J., Wang, X., Zhang, Y. et al. Time Series Prediction Method Based on Variant LSTM Recurrent Neural Network. Neural Process Lett 52, 1485–1500 (2020). https://doi.org/10.1007/s11063-020-10319-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11063-020-10319-3