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
In long-term time series forecasting (LTSF) tasks, existing deep learning
models overlook the crucial characteristic that discrete time series originate
from underlying continuous dynamic systems, resulting in a lack of
extrapolation and evolution capabilities. Recognizing the chaotic nature of
real-world data, our model, \textbf{\textit{Attraos}}, incorporates chaos
theory into LTSF, perceiving real-world time series as observations from
unknown high-dimensional chaotic dynamic systems. Under the concept of
attractor invariance, Attraos utilizes the proposed multi-scale dynamic memory
unit to memorize historical dynamics structure and predicts 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.