Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective
Jiaxi Hu, Yuehong HU, Wei Chen, Ming Jin, Shirui Pan, Qingsong Wen, Yuxuan Liang
Abstract
In long-term time series forecasting (LTSF) tasks, an increasing number of works have acknowledged that discrete time series originate from continuous dynamic systems and have attempted to model their underlying dynamics. Recognizing the chaotic nature of real-world data, our model, Attraos, incorporates chaos theory into LTSF, perceiving real-world time series as low-dimensional observations from unknown high-dimensional chaotic dynamical systems. Under the concept of attractor invariance, Attraos utilizes non-parametric Phase Space Reconstruction embedding along with a novel multi-resolution dynamic memory unit to memorize historical dynamical structures, and evolves by a frequency-enhanced local evolution strategy. Detailed theoretical analysis and abundant empirical evidence consistently show that Attraos outperforms various LTSF methods on mainstream LTSF datasets and chaotic datasets with only one-twelfth of the parameters compared to PatchTST.
BibTeX
@inproceedings{
hu2024attractor,
title={Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective},
author={Jiaxi Hu and Yuehong HU and Wei Chen and Ming Jin and Shirui Pan and Qingsong Wen and Yuxuan Liang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fEYHZzN7kX}
}