Irregularly sampled multivariate time series (ISMTS) are prevalent in reality. Due to their non-uniform intervals between successive observations and varying sampling rates among series, the channel-independent (CI) strategy, which has been demonstrated more desirable for complete multivariate time series forecasting in recent studies, has failed. This failure can be further attributed to the sampling sparsity, which provides insufficient information for effective CI learning, thereby reducing its capacity. When we resort to the channel-dependent (CD) strategy, even higher capacity cannot mitigate the potential loss of diversity in learning similar embedding patterns across different channels. We find that existing work considers CI and CD strategies to be mutually exclusive, primarily because they apply these strategies to the global channel. However, we hold the view that channel strategies do not necessarily have to be used globally. Instead, by appropriately applying them locally and globally, we can create an opportunity to take full advantage of both strategies. This leads us to introduce the Channel Harmony ISMTS Transformer (TimeCHEAT), which utilizes the CD locally and the CI globally. Specifically, we segment the ISMTS into sub-series level patches. Locally, the CD strategy aggregates information within each patch for time embedding learning, maximizing the use of relevant observations while reducing long-range irrelevant interference. Here, we enhance generality by transforming embedding learning into an edge weight prediction task using bipartite graphs, eliminating the need for special prior knowledge. Globally, the CI strategy is applied across patches, allowing the Transformer to learn individualized attention patterns for each channel. Experimental results indicate our proposed TimeCHEAT demonstrates competitive SOTA performance across three mainstream tasks.

翻译：不规则采样多元时间序列在现实中普遍存在。由于连续观测点之间的间隔不均匀以及序列间采样率不同，近年来研究证明对完整多元时间序列预测更有效的通道独立策略在此失效。这一失败可进一步归因于采样稀疏性，其无法为有效的通道独立学习提供足够信息，从而降低了该策略的能力。当我们转而采用通道依赖策略时，即使更高的模型容量也无法缓解跨不同通道学习相似嵌入模式可能导致的多样性损失。我们发现现有工作将通道独立与通道依赖策略视为互斥，主要是因为它们在全局通道层面应用这些策略。然而，我们认为通道策略未必需要全局统一使用。相反，通过在局部和全局恰当地应用它们，我们能够创造充分利用两种策略优势的机会。这促使我们提出通道和谐不规则采样多元时间序列Transformer模型，该模型在局部使用通道依赖策略，在全局使用通道独立策略。具体而言，我们将不规则采样多元时间序列分割为子序列级别的片段。在局部，通道依赖策略聚合每个片段内的信息以进行时间嵌入学习，在最大化利用相关观测的同时减少长程无关干扰。在此，我们通过将嵌入学习转化为使用二分图的边权重预测任务来增强通用性，无需特殊先验知识。在全局，通道独立策略应用于跨片段层面，使Transformer能够为每个通道学习个性化的注意力模式。实验结果表明，我们提出的TimeCHEAT在三个主流任务上均展现出具有竞争力的最先进性能。