结构知识引导的持续多元时间序列预测
Structural Knowledge Informed Continual Multivariate Time Series Forecasting
AI总结：
本文提出结构知识引导持续学习框架SKI-CL，通过图结构学习与一致性正则化建模不同机制下的变量依赖，并结合表示匹配记忆回放缓解灾难性遗忘，提升持续多元时间序列预测性能。
摘要：
近期关于多元时间序列（MTS）预测的研究表明，显式建模不同时间序列之间的隐式依赖关系能够带来有前景的预测性能和可靠的可解释性。然而，当MTS在不同机制（阶段）下被持续积累时，变量依赖关系的建模仍未得到充分探索。由于可能存在分布和依赖关系的差异，底层模型可能遭遇灾难性遗忘问题，即在保持预测性能的同时，难以记忆和推断不同机制下不同类型的变量依赖关系。为了解决这一问题，我们提出了一种新颖的结构知识引导持续学习（SKI-CL）框架，在持续学习范式下执行MTS预测，该框架利用结构知识引导预测模型识别并适应不同机制，并从每个机制中选择具有代表性的MTS样本用于记忆回放。具体而言，我们开发了一种基于图结构学习的预测模型，在优化MTS数据上的预测目标的同时，在学习到的变量依赖关系与结构知识之间施加一致性正则化方案。这样，每个机制中学习到的MTS表示都与不同的结构知识相关联，这有助于模型记忆各种可能的情景，并在持续学习情境下产生准确的预测结果。同时，我们开发了一种表示匹配的记忆回放方案，该方案最大化MTS数据的时间覆盖范围，以高效保留每个机制的潜在时间动态和依赖结构。在合成基准和真实世界基准上的充分实证研究验证了SKI-CL在持续MTS预测任务中的有效性及其相对于最先进方法的优势。
英文摘要：
Recent studies in multivariate time series (MTS) forecasting reveal that explicitly modeling the hidden dependencies among different time series can yield promising forecasting performance and reliable explanations. However, modeling variable dependencies remains underexplored when MTS is continuously accumulated under different regimes (stages). Due to the potential distribution and dependency disparities, the underlying model may encounter the catastrophic forgetting problem, i.e., it is challenging to memorize and infer different types of variable dependencies across different regimes while maintaining forecasting performance. To address this issue, we propose a novel Structural Knowledge Informed Continual Learning (SKI-CL) framework to perform MTS forecasting within a continual learning paradigm, which leverages structural knowledge to steer the forecasting model toward identifying and adapting to different regimes, and selects representative MTS samples from each regime for memory replay. Specifically, we develop a forecasting model based on graph structure learning, where a consistency regularization scheme is imposed between the learned variable dependencies and the structural knowledge while optimizing the forecasting objective over the MTS data. As such, MTS representations learned in each regime are associated with distinct structural knowledge, which helps the model memorize a variety of conceivable scenarios and results in accurate forecasts in the continual learning context. Meanwhile, we develop a representation-matching memory replay scheme that maximizes the temporal coverage of MTS data to efficiently preserve the underlying temporal dynamics and dependency structures of each regime. Thorough empirical studies on synthetic and real-world benchmarks validate SKI-CL's efficacy and advantages over the state-of-the-art for continual MTS forecasting tasks.
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