用大模型提升时间序列对未知数据的适应能力
Tri-Level Navigator: LLM-Empowered Tri-Level Learning for Time Series OOD Generalization
- 设计三层次学习框架，同时考虑样本与分组层面的不确定性
- 理论证明算法收敛，达到ε精度需迭代O(1/ε²)次
- 适合关注时序数据泛化能力的开发者与研究者
机器学习中的分布外（OOD）泛化是新兴研究方向，旨在提升模型在面对与训练数据显著不同、可能带有对抗性的新数据时的适应性与鲁棒性。本文通过预训练大语言模型（LLM）研究时间序列的OOD泛化问题。提出一种新的三层次学习框架TTSO，同时考虑样本级和组级不确定性，为OOD泛化问题提供全新的理论视角。进一步设计了一种分层定位算法，理论上证明了该算法的收敛性，且获得ε-驻点的迭代复杂度被限制在O(1/ε²)。在真实世界数据集上的大量实验验证了所提方法的有效性。
原文摘要 · Abstract (English)
Out-of-Distribution (OOD) generalization in machine learning is a burgeoning area of study. Its primary goal is to enhance the adaptability and resilience of machine learning models when faced with new, unseen, and potentially adversarial data that significantly diverges from their original training datasets. In this paper, we investigate time series OOD generalization via pre-trained Large Language Models (LLMs). We first propose a novel \textbf{T}ri-level learning framework for \textbf{T}ime \textbf{S}eries \textbf{O}OD generalization, termed TTSO, which considers both sample-level and group-level uncertainties. This formula offers a fresh theoretic perspective for formulating and analyzing OOD generalization problem. In addition, we provide a theoretical analysis to justify this method is well motivated. We then develop a stratified localization algorithm tailored for this tri-level optimization problem, theoretically demonstrating the guaranteed convergence of the proposed algorithm. Our analysis also reveals that the iteration complexity to obtain an $ε$-stationary point is bounded by O($\frac{1}{ε^{2}}$). Extensive experiments on real-world datasets have been conducted to elucidate the effectiveness of the proposed method.
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