在线时间序列预测中未知干预下的长短时状态解耦
Disentangling Long-Short Term State Under Unknown Interventions for Online Time Series Forecasting
AI总结：
针对在线时间序列预测中长短时变化难以区分的问题，本文提出未知干预下的状态解耦框架与LSTD模型，并通过可辨识性理论及双重约束提升预测性能。
摘要：
当前时间序列预测方法在在线场景中表现吃力，因为当数据依次到达时，很难在适应短期变化的同时保留长期依赖。尽管近期一些方法通过控制潜在状态的更新来解决这一问题，但它们无法解耦长期状态与短期状态，从而不能有效适应非平稳性。为应对这一挑战，我们提出一个通用框架，用于在线时间序列预测中的长期/短期状态解耦。我们的想法受到如下观察启发：短期变化可能由未知干预所引发，例如股票市场中的突发政策。基于这一洞见，我们形式化了一个对短期状态存在未知干预的数据生成过程。在温和假设下，我们进一步利用由未知干预导致的短期状态的独立性，建立可辨识性理论，以实现长期/短期状态的解耦。在该理论基础上，我们开发了长短时解耦模型（LSTD），分别通过长期编码器和短期编码器提取长期状态与短期状态。此外，LSTD模型引入平滑约束以保留长期依赖，并引入中断依赖约束以强制遗忘短期依赖，二者共同促进长期/短期状态的解耦。在多个基准数据集上的实验结果表明，我们的LSTD模型在在线时间序列预测方面优于现有方法，验证了其在真实世界应用中的有效性。
英文摘要：
Current methods for time series forecasting struggle in the online scenario, since it is difficult to preserve long-term dependency while adapting short-term changes when data are arriving sequentially. Although some recent methods solve this problem by controlling the updates of latent states, they cannot disentangle the long/short-term states, leading to the inability to effectively adapt to nonstationary. To tackle this challenge, we propose a general framework to disentangle long/short-term states for online time series forecasting. Our idea is inspired by the observations where short-term changes can be led by unknown interventions like abrupt policies in the stock market. Based on this insight, we formalize a data generation process with unknown interventions on short-term states. Under mild assumptions, we further leverage the independence of short-term states led by unknown interventions to establish the identification theory to achieve the disentanglement of long/short-term states. Built on this theory, we develop a long short-term disentanglement model (LSTD) to extract the long/short-term states with long/short-term encoders, respectively. Furthermore, the LSTD model incorporates a smooth constraint to preserve the long-term dependencies and an interrupted dependency constraint to enforce the forgetting of short-term dependencies, together boosting the disentanglement of long/short-term states. Experimental results on several benchmark datasets show that our \textbf{LSTD} model outperforms existing methods for online time series forecasting, validating its efficacy in real-world applications.
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。
