LiNo：推进线性与非线性模式的递归残差分解以实现稳健的时间序列预测
LiNo: Advancing Recursive Residual Decomposition of Linear and Nonlinear Patterns for Robust Time Series Forecasting
AI总结：
针对时间序列预测中线性与非线性模式分离不足的问题，提出LiNo框架，通过递归残差分解显式提取两类模式，在13个真实基准上达到最先进性能。
摘要：
预测模型在以数据驱动的世界中至关重要，因为海量的时间序列数据表现为线性与非线性模式的复合体。近年来的深度时间序列预测模型在利用季节性和趋势分解来分离纠缠成分方面存在困难。这种策略仅显式提取诸如趋势之类的简单线性模式，而将其他线性模式以及大量未被探索的非线性模式留给了残差。其有缺陷的线性和非线性特征提取模型以及浅层分解限制了它们对现实场景中多样模式的适应能力。鉴于此，我们通过引入对线性和非线性模式的显式提取来创新递归残差分解。这一更深层次的分解框架名为LiNo，使用Li块（可以是移动平均核）捕获线性模式，并使用No块（可以是Transformer编码器）对非线性模式进行建模。这两种模式的提取交替且递归地进行。为了充分发挥LiNo的潜力，我们将当前简单的线性模式提取器发展为通用的可学习自回归模型，并设计了一种能够处理所有关键非线性模式的新型No块。值得注意的是，所提出的LiNo在单变量和多变量预测场景下的十三个真实世界基准上达到了最先进的性能。实验表明，通过这种先进的递归残差分解，当前的预测模型可以提供更稳健和更精确的结果。我们希望这项工作能为设计更有效的预测模型提供见解。代码可在以下仓库获取：https://github.com/Levi-Ackman/LiNo。
英文摘要：
Forecasting models are pivotal in a data-driven world with vast volumes of time series data that appear as a compound of vast Linear and Nonlinear patterns. Recent deep time series forecasting models struggle to utilize seasonal and trend decomposition to separate the entangled components. Such a strategy only explicitly extracts simple linear patterns like trends, leaving the other linear modes and vast unexplored nonlinear patterns to the residual. Their flawed linear and nonlinear feature extraction models and shallow-level decomposition limit their adaptation to the diverse patterns present in real-world scenarios. Given this, we innovate Recursive Residual Decomposition by introducing explicit extraction of both linear and nonlinear patterns. This deeper-level decomposition framework, which is named LiNo, captures linear patterns using a Li block which can be a moving average kernel, and models nonlinear patterns using a No block which can be a Transformer encoder. The extraction of these two patterns is performed alternatively and recursively. To achieve the full potential of LiNo, we develop the current simple linear pattern extractor to a general learnable autoregressive model, and design a novel No block that can handle all essential nonlinear patterns. Remarkably, the proposed LiNo achieves state-of-the-art on thirteen real-world benchmarks under univariate and multivariate forecasting scenarios. Experiments show that current forecasting models can deliver more robust and precise results through this advanced Recursive Residual Decomposition. We hope this work could offer insight into designing more effective forecasting models. Code is available at this Repository: https://github.com/Levi-Ackman/LiNo.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
