学习递归多尺度表示以进行不规则多变量时间序列预测
Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting
AI总结：
ReIMTS通过递归多尺度建模方法，无需重采样即可提升不规则多变量时间序列预测的准确性。
摘要：
不规则多变量时间序列（IMTS）的特点是连续时间戳之间的间隔不均匀，这种采样模式信息对于学习时间和变量依赖性具有价值和信息。此外，IMTS往往在多个时间尺度上表现出多样的依赖性。然而，许多现有的多尺度IMTS方法使用重采样来获得粗略序列，这会改变原始时间戳并破坏采样模式信息。为了解决这一挑战，我们提出了ReIMTS，一种用于不规则多变量时间序列预测的递归多尺度建模方法。与重采样不同，ReIMTS保持时间戳不变，并递归地将每个样本分割成具有逐步缩短时间周期的子样本。基于这些长到短子样本中的原始采样时间戳，提出了一种不规则性感知的表示融合机制，以捕捉全局到局部的依赖性，以实现准确的预测。广泛的实验表明，在不同的模型和现实世界数据集上，预测任务的平均性能提高了27.1%。我们的代码可在https://github.com/Ladbaby/PyOmniTS上获得。
英文摘要：
Irregular Multivariate Time Series (IMTS) are characterized by uneven intervals between consecutive timestamps, which carry sampling pattern information valuable and informative for learning temporal and variable dependencies. In addition, IMTS often exhibit diverse dependencies across multiple time scales. However, many existing multi-scale IMTS methods use resampling to obtain the coarse series, which can alter the original timestamps and disrupt the sampling pattern information. To address the challenge, we propose ReIMTS, a Recursive multi-scale modeling approach for Irregular Multivariate Time Series forecasting. Instead of resampling, ReIMTS keeps timestamps unchanged and recursively splits each sample into subsamples with progressively shorter time periods. Based on the original sampling timestamps in these long-to-short subsamples, an irregularity-aware representation fusion mechanism is proposed to capture global-to-local dependencies for accurate forecasting. Extensive experiments demonstrate an average performance improvement of 27.1\% in the forecasting task across different models and real-world datasets. Our code is available at https://github.com/Ladbaby/PyOmniTS.
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。
