A TCN-Linear Hybrid Model for Chaotic Time Series Forecasting

The applications of deep learning and artificial intelligence have permeated daily life, with time series prediction emerging as a focal area of research due to its significance in data analysis. The evolution of deep learning methods for time series prediction has progressed from the Convolutional...

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Bibliographic Details
Published in:	Entropy (Basel, Switzerland) Vol. 26; no. 6; p. 467
Main Authors:	Wang, Mengjiao, Qin, Fengtai
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 29-05-2024 MDPI
Subjects:	Accuracy Alternative energy sources Artificial intelligence Artificial neural networks chaos prediction Computational linguistics Data analysis Decomposition Deep learning Forecasting Internet service providers Language processing Lorenz, Edward N Machine learning Natural language interfaces Neural networks Recurrent neural networks Time series time series forecasting Transformers United States chaos prediction time series forecasting neural networks
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Summary:	The applications of deep learning and artificial intelligence have permeated daily life, with time series prediction emerging as a focal area of research due to its significance in data analysis. The evolution of deep learning methods for time series prediction has progressed from the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN) to the recently popularized Transformer network. However, each of these methods has encountered specific issues. Recent studies have questioned the effectiveness of the self-attention mechanism in Transformers for time series prediction, prompting a reevaluation of approaches to LTSF (Long Time Series Forecasting) problems. To circumvent the limitations present in current models, this paper introduces a novel hybrid network, Temporal Convolutional Network-Linear (TCN-Linear), which leverages the temporal prediction capabilities of the Temporal Convolutional Network (TCN) to enhance the capacity of LSTF-Linear. Time series from three classical chaotic systems (Lorenz, Mackey-Glass, and Rossler) and real-world stock data serve as experimental datasets. Numerical simulation results indicate that, compared to classical networks and novel hybrid models, our model achieves the lowest RMSE, MAE, and MSE with the fewest training parameters, and its R value is the closest to 1.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1099-4300 1099-4300
DOI:	10.3390/e26060467