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Neural Processing Letters

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07-10-2022

A New Time Series Forecasting Model Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Temporal Convolutional Network

Authors: Chen Guo, Xumin Kang, Jianping Xiong, Jianhua Wu

Published in: Neural Processing Letters | Issue 4/2023

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Abstract

In this paper, a new hybrid time series forecasting model based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a temporal convolutional network (TCN) (CEEMDAN-TCN) is proposed. The CEEMDAN is used to decompose the time series data and the TCN is used to obtain a good prediction accuracy. The effectiveness of the model is verified in univariate and multivariate time series forecasting tasks. The experimental results indicate that compared with the long short-term memory model and other hybrid models, the proposed CEEMDAN-TCN model shows a better performance in both univariate and multivariate prediction tasks.

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Title: A New Time Series Forecasting Model Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Temporal Convolutional Network
Authors: Chen Guo
Xumin Kang
Jianping Xiong
Jianhua Wu
Publication date: 07-10-2022
Publisher: Springer US
Published in: Neural Processing Letters / Issue 4/2023
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-022-11046-7

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