  In long-term time series forecasting (LTSF) tasks, an increasing number of
models have acknowledged that discrete time series originate from continuous
dynamic systems and have attempted to model their dynamical structures.
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
non-parametric Phase Space Reconstruction embedding and 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 with only one-twelfth of the parameters compared to PatchTST.
