Academic Journal
A TCN-Linear Hybrid Model for Chaotic Time Series Forecasting
| Title: | A TCN-Linear Hybrid Model for Chaotic Time Series Forecasting |
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| Authors: | Mengjiao Wang, Fengtai Qin |
| Source: | Entropy, Vol 26, Iss 6, p 467 (2024) |
| Publisher Information: | MDPI AG, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Science LCC:Astrophysics LCC:Physics |
| Subject Terms: | chaos prediction, time series forecasting, neural networks, Science, Astrophysics, QB460-466, Physics, QC1-999 |
| More Details: | 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 R2 value is the closest to 1. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1099-4300 |
| Relation: | https://www.mdpi.com/1099-4300/26/6/467; https://doaj.org/toc/1099-4300 |
| DOI: | 10.3390/e26060467 |
| Access URL: | https://doaj.org/article/cf898c13a97d4a0884b9ac8411518721 |
| Accession Number: | edsdoj.f898c13a97d4a0884b9ac8411518721 |
| Database: | Directory of Open Access Journals |
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| ISSN: | 10994300 |
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| DOI: | 10.3390/e26060467 |
| Published in: | Entropy |
| Language: | English |