Time-series forecasting (TSF) finds broad applications in real-world
scenarios. Due to the dynamic nature of time-series data, it is crucial to
equip TSF models with out-of-distribution (OOD) generalization abilities, as
historical training data and future test data can have different distributions.
In this paper, we aim to alleviate the inherent OOD problem in TSF via
invariant learning. We identify fundamental challenges of invariant learning
for TSF. First, the target variables in TSF may not be sufficiently determined
by the input due to unobserved core variables in TSF, breaking the conventional
assumption of invariant learning. Second, time-series datasets lack adequate
environment labels, while existing environmental inference methods are not
suitable for TSF.
To address these challenges, we propose FOIL, a model-agnostic framework that
enables timeseries Forecasting for Out-of-distribution generalization via
Invariant Learning. FOIL employs a novel surrogate loss to mitigate the impact
of unobserved variables. Further, FOIL implements a joint optimization by
alternately inferring environments effectively with a multi-head network while
preserving the temporal adjacency structure, and learning invariant
representations across inferred environments for OOD generalized TSF. We
demonstrate that the proposed FOIL significantly improves the performance of
various TSF models, achieving gains of up to 85%.

通过不变性学习，提出了 FOIL 框架，该框架能够增强时间序列预测模型在分布不同的情况下具有的广义能力，改进性能可达 85%。

利用不变学习的时间序列预测方法进行超出分布泛化

Time-Series Forecasting for Out-of-Distribution Generalization Using  Invariant Learning

Several queries and scores have recently been proposed to explain individual
predictions over ML models. Given the need for flexible, reliable, and
easy-to-apply interpretability methods for ML models, we foresee the need for
developing declarative languages to naturally specify different explainability
queries. We do this in a principled way by rooting such a language in a logic,
called FOIL, that allows for expressing many simple but important
explainability queries, and might serve as a core for more expressive
interpretability languages. We study the computational complexity of FOIL
queries over two classes of ML models often deemed to be easily interpretable:
decision trees and OBDDs. Since the number of possible inputs for an ML model
is exponential in its dimension, the tractability of the FOIL evaluation
problem is delicate but can be achieved by either restricting the structure of
the models or the fragment of FOIL being evaluated. We also present a prototype
implementation of FOIL wrapped in a high-level declarative language and perform
experiments showing that such a language can be used in practice.

本研究基于 FOIL 逻辑构建了声明式语言用于自然特定不同解释性查询，并且研究了 FOIL 查询的计算复杂度，为解释可解释的 ML 模型提供了可行的路径。