模块化深度学习用于多变量时间序列：解耦插值与下游任务
Modular Deep Learning for Multivariate Time-Series: Decoupling Imputation and Downstream Tasks
AI总结：
本文提出模块化深度学习方法，解耦时间序列插值与下游任务，提升灵活性和可重用性，适用于多变量时间序列分析。
摘要：
大规模时间序列数据中缺失值普遍存在，给可靠分析和决策带来挑战。许多神经网络架构已被设计用来建模和插值此类数据复杂的异质缺失模式。大多数现有方法是端到端的，导致插值与下游预测任务紧密耦合，限制了训练模型的再利用性，降低了可解释性，并在评估模型质量时带来挑战。在本文中，我们呼吁一种解耦插值与下游任务的模块化方法，使两者能够独立优化，从而获得更大的适应性。使用最大的开源Python库PyPOTS，我们评估了六个最先进的模型在七个跨多个领域的数据集上进行插值和预测的模块化流水线。我们的结果表明，模块化方法在保持高性能的同时，优先考虑了灵活性和再利用性——这些特性对于实际应用至关重要。通过这项工作，我们旨在展示模块化如何有助于多变量时间序列分析，实现性能与适应性之间的平衡。
英文摘要：
Missing values are pervasive in large-scale time-series data, posing challenges for reliable analysis and decision-making. Many neural architectures have been designed to model and impute the complex and heterogeneous missingness patterns of such data. Most existing methods are end-to-end, rendering imputation tightly coupled with downstream predictive tasks and leading to limited reusability of the trained model, reduced interpretability, and challenges in assessing model quality. In this paper, we call for a modular approach that decouples imputation and downstream tasks, enabling independent optimisation and greater adaptability. Using the largest open-source Python library for deep learning-based time-series analysis, PyPOTS, we evaluate a modular pipeline across six state-of-the-art models that perform imputation and prediction on seven datasets spanning multiple domains. Our results show that a modular approach maintains high performance while prioritising flexibility and reusability - qualities that are crucial for real-world applications. Through this work, we aim to demonstrate how modularity can benefit multivariate time-series analysis, achieving a balance between performance and adaptability.
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。
