不重采样直接分层建模，提升不规则多变量时间序列预测精度
Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting
- 递归拆分原始数据，保留真实时间戳不变
- 跨尺度融合捕捉全局到局部依赖，平均性能提升27.1%
- 适合采样不规则、多时频依赖的工业/医疗时间序列场景
不规则多变量时间序列（IMTS）具有非均匀的时间间隔，这些间隔蕴含采样模式信息，对学习时序与变量间依赖关系至关重要。此外，IMTS常表现出跨多个时间尺度的多样依赖性。然而，现有方法多通过重采样生成粗粒度序列，会改变原始时间戳并破坏采样模式信息。为此，我们提出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。该方法不进行重采样，而是保持时间戳不变，将每个样本递归拆分为时间跨度逐级缩短的子样本。基于这些长至短子样本中的原始采样时间戳，设计了不规则感知表示融合机制，以捕获从全局到局部的依赖关系，实现精准预测。大量实验表明，在不同模型和真实数据集上，预测任务平均性能提升27.1%。代码已开源：https://github.com/Ladbaby/PyOmniTS。
原文摘要 · Abstract (English)
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.
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