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Novel hybrid multi-head self-attention and multifractal algorithm for non-stationary time series prediction.
- Source :
-
Information Sciences . Oct2022, Vol. 613, p541-555. 15p. - Publication Year :
- 2022
-
Abstract
- Traditional time series prediction methods have shown their outstanding capabilities in time series prediction. However, due to essential differences in volatility characteristics among diverse types of non-stationary multivariate time series (NSMTS), it is difficult for traditional methods to maintain robust prediction performance. This study proposes a novel dynamic recurrent neural network to achieve stable and robust prediction performance. First, a multifractal gated recurrent unit (MF-GRU) based on the multifractal method is proposed to extract volatility characteristics. Meanwhile, to strengthen the parameters of the historical hidden layer state that has a more significant impact on the output, a self-attention mechanism is introduced into the MF-GRU, leading to a multifractal gated recurrent unit multi-head self-attention model. The efficiency of the proposed model was verified on public datasets. The experimental results show that the proposed model outperforms the traditional methods, such as long short-term memory (LSTM), the gated recurrent unit (GRU), and the minimal gated unit (MGU). etc. [ABSTRACT FROM AUTHOR]
- Subjects :
- *TIME series analysis
*RECURRENT neural networks
*FORECASTING
*BLENDED learning
Subjects
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 613
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 159928210
- Full Text :
- https://doi.org/10.1016/j.ins.2022.08.126