Time series prediction method for continuous spares requirement based on recursive decomposition of information compression matrix

Time series data from real problems have nonlinear, non-smooth, and multi-scale composite characteristics. This paper first proposes a gated recurrent unit-correction (GRU-corr) network model, which adds a correction layer to the GRU neural network. Then, a adaptive staged variation PSO (ASPSO) is proposed. Finally, to overcome the drawbacks of the imprecise selection of the GRU-corr network parameters and obtain the high-precision global optimization of network parameters, weight parameters and the hidden nodes number of GRU-corr is optimized by ASPSO, and a time series prediction model (ASPSO-GRU-corr) is proposed based on the GRU-corr optimized by ASPSO. In the experiment, a comparative analysis of the optimization performance of ASPSO on a benchmark function was performed to verify its validity, and then the ASPSO-GRU-corr model is used to predict the ship motion cross-sway angle data. The results show that, ASPSO has better optimization performance and convergence speed compared with other algorithms, while the ASPSO-GRU-corr has higher generalization performance and lower architecture complexity. The ASPSO-GRU-corr can reveal the intrinsic multi-scale composite features of the time series, which is a reliable nonlinear and non-steady time series prediction method.

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Time Series Prediction Method Based on Variant LSTM Recurrent Neural Network

Neural Processing Letters ◽

10.1007/s11063-020-10319-3 ◽

2020 ◽

Vol 52 (2) ◽

pp. 1485-1500

Author(s):

Jiaojiao Hu ◽

Xiaofeng Wang ◽

Ying Zhang ◽

Depeng Zhang ◽

Meng Zhang ◽

...

Keyword(s):

Neural Network ◽

Time Series ◽

Recurrent Neural Network ◽

Time Series Prediction ◽

Prediction Method

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Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations

Chinese Physics B ◽

10.1088/1674-1056/25/11/110502 ◽

2016 ◽

Vol 25 (11) ◽

pp. 110502 ◽

Cited By ~ 1

Author(s):

Jian Zhong ◽

Gang Dong ◽

Yimei Sun ◽

Zhaoyang Zhang ◽

Yuqin Wu

Keyword(s):

Genetic Algorithm ◽

Time Series ◽

South China Sea ◽

South China ◽

Time Series Prediction ◽

Surface Wind ◽

Prediction Method ◽

Nonlinear Time Series ◽

The South China Sea ◽

China Sea

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A Prediction Method for Nonlinear Correlative Time Series

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.40-41.930 ◽

2010 ◽

Vol 40-41 ◽

pp. 930-936 ◽

Cited By ~ 1

Author(s):

Cong Gui Yuan ◽

Xin Zheng Zhang ◽

Shu Qiong Xu

Keyword(s):

Neural Network ◽

Time Series ◽

Mutual Information ◽

Orthogonal Polynomial ◽

Time Series Prediction ◽

Prediction Method ◽

Nonlinear Time Series ◽

Iterative Learning ◽

Polynomial Basis

A nonlinear correlative time series prediction method is presented in this paper.It is based on the mutual information of time series and orthogonal polynomial basis neural network. Inputs of network are selected by mutual information, and orthogonal polynomial basis is used as active function.The network is trained by an error iterative learning algorithm.This proposed method for nonlinear time series is tested using two well known time series prediction problems:Gas furnace data time series and Mackey-Glass time series.

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