用分层树结构同时捕捉时间序列的全局趋势与局部细节，提升预测精度。
PRISM: A hierarchical multiscale approach for time series forecasting
- 构建可学习的树状分层结构，逐级分解信号以捕获多尺度特征。
- 在多个数据集上优于现有最优模型，显著提升预测准确率。
- 适合需要处理复杂多尺度时间序列的金融、医疗等领域应用。
时间序列预测在金融、生物和医疗等领域至关重要。尽管已有进展，但真实世界的时间序列同时包含全局趋势、局部精细结构以及中间尺度的特征，使得精准预测仍具挑战。本文提出一种新方法PRISM（Partitioned Representation for Iterative Sequence Modeling），通过可学习的树状分层对信号进行划分：树根捕获信号的粗粒度趋势，递归分裂揭示更局部的视图。每一层级将数据投影到时频基（如小波或指数移动平均）以提取尺度特定特征，并在层次结构中聚合。该设计使模型能联合建模信号的全局结构与局部动态，实现高精度预测。在多个基准数据集上的实验表明，该方法优于当前最先进模型。结果表明，该分层框架为多变量时间序列预测提供了一个轻量且灵活的解决方案。代码已开源：https://github.com/nerdslab/prism。
原文摘要 · Abstract (English)
Forecasting is critical in areas such as finance, biology, and healthcare. Despite the progress in the field, making accurate forecasts remains challenging because real-world time series contain both global trends, local fine-grained structure, and features on multiple scales in between. Here, we present a new forecasting method, PRISM (Partitioned Representation for Iterative Sequence Modeling), that addresses this challenge through a learnable tree-based partitioning of the signal. At the root of the tree, a global representation captures coarse trends in the signal, while recursive splits reveal increasingly localized views of the signal. At each level of the tree, data are projected onto a time-frequency basis (e.g., wavelets or exponential moving averages) to extract scale-specific features, which are then aggregated across the hierarchy. This design allows the model to jointly capture global structure and local dynamics of the signal, enabling accurate forecasting. Experiments across benchmark datasets show that our method outperforms state-of-the-art methods for forecasting. Overall, these results demonstrate that our hierarchical approach provides a lightweight and flexible framework for forecasting multivariate time series. The code is available at https://github.com/nerdslab/prism.
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