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Open AccessArticle

Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting

1
Nano-Optical Material and Storage Device Research Center, School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China
2
State Key Laboratory of Powder Metallurgy, School of Physics and Electronics, Central South University, Changsha 410083, China
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(8), 876; https://doi.org/10.3390/electronics8080876
Received: 7 July 2019 / Revised: 2 August 2019 / Accepted: 5 August 2019 / Published: 7 August 2019
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Abstract

Multivariable time series prediction has been widely studied in power energy, aerology, meteorology, finance, transportation, etc. Traditional modeling methods have complex patterns and are inefficient to capture long-term multivariate dependencies of data for desired forecasting accuracy. To address such concerns, various deep learning models based on Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) methods are proposed. To improve the prediction accuracy and minimize the multivariate time series data dependence for aperiodic data, in this article, Beijing PM2.5 and ISO-NE Dataset are analyzed by a novel Multivariate Temporal Convolution Network (M-TCN) model. In this model, multi-variable time series prediction is constructed as a sequence-to-sequence scenario for non-periodic datasets. The multichannel residual blocks in parallel with asymmetric structure based on deep convolution neural network is proposed. The results are compared with rich competitive algorithms of long short term memory (LSTM), convolutional LSTM (ConvLSTM), Temporal Convolution Network (TCN) and Multivariate Attention LSTM-FCN (MALSTM-FCN), which indicate significant improvement of prediction accuracy, robust and generalization of our model. View Full-Text
Keywords: deep learning; multivariate time series forecasting; multivariate temporal convolutional network deep learning; multivariate time series forecasting; multivariate temporal convolutional network
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Wan, R.; Mei, S.; Wang, J.; Liu, M.; Yang, F. Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting. Electronics 2019, 8, 876.

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