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Time Series Forecasting Using GRU Neural Network with Multi-lag After Decomposition

2017; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-319-70139-4_53

1611-3349

Xu Zhang, Furao Shen, Jinxi Zhao, GuoHai Yang,

Stock Market Forecasting Methods

Time series forecasting has a wide range of applications in society, industry, market, etc. In this paper, a new time series forecasting method (FCD-MLGRU) is proposed for solving short-term forecasting problem. First we decompose the original time series using Filtering Cycle Decomposition (FCD) proposed in this paper, secondly we train the Gated Recurrent Unit (GRU) Neural Network to forecasting the subseries respectively. In the process of training and forecasting, the multi-time-lag sampling and ensemble forecasting method is adopted, which reduces the dependence on the selection of time lag and enhance the generalization and stability of the model. The comparative experiments on the real data sets and theoretical analysis show that our proposed method performs better than other related methods.