Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective
CoRR(2024)
摘要
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, 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.
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