DOI: 10.14704/nq.2018.16.6.1585

A New BP Neural Network Model for the Prediction Problem of Equally Spaced Time Sequences and Its Application

Mengxia Li, Ruiquan Liao, Yong Dong

Abstract


For the prediction of equally spaced time sequences, this paper proposes a new construction method for training datasets based on the method which is used to determine parameters of the ARIMA model and builds a new BP neural network predictive model. For the actual data of annual power consumption in China from 1980 to 2016, data from 1980 to 2013 are chosen for this paper to construct the training dataset, and then the model proposed in this paper and the standard BP model are used to predict the power consumption from 2014 to 2016. Finally, after comparing the results obtained from the model proposed in this paper and the standard BP model, the prediction accuracy obtained by the model used here is found to be no more than 2%.

Keywords


BP Neural Network, Time window translation, ARIMA model

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References


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Supporting Agencies

The authors will thank people in the Branch of Key Laboratory of CNPC for Oil and Gas Production for their great help. This paper is supported by National Natural Science Foundation of China (61572084 and 51504038), National Key S&T Special Projects (2016



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