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.

在长期时间序列预测任务中，现有的深度学习模型忽视了离散时间序列起源于连续动态系统的关键特性，导致缺乏外推和演化能力。我们的模型 'Attraos' 将混沌理论引入长期时间序列预测中，将真实世界时间序列视为未知高维混沌动态系统的观测。在吸引子不变性的概念下，Attraos 利用提出的多尺度动态记忆单元来记忆历史动态结构，并通过频率增强的局部演化策略进行预测。详细的理论分析和丰富的实证证据一致表明，Attraos 在主流长期时间序列预测数据集和混沌数据集上优于各种长期时间序列预测方法。