将时间序列分解拆解为可定制模块，提升分析灵活性与可解释性。
StructuralDecompose: A Modular Framework for Robust Time Series Decomposition in R
- 分模块设计：拆解为变点、异常、平滑与分解四部分独立处理
- 在真实与模拟数据上表现优于Rbeast和autostsm等主流工具
- 适合需要可解释性的时间序列分析场景，如金融、医疗监控
我们提出StructuralDecompose，一个用于模块化、可解释时间序列分解的R包。不同于传统将分解视为整体过程的方法，StructuralDecompose将其分解为变点检测、异常检测、平滑和分解四个独立组件，提升灵活性与鲁棒性，使用户可根据时间序列特性自由选择方法。我们在模拟数据和真实数据集上验证了该包性能，对比了Rbeast、autostsm等先进工具，并探讨其在可解释机器学习流程中的应用价值。
原文摘要 · Abstract (English)
We present StructuralDecompose, an R package for modular and interpretable time series decomposition. Unlike existing approaches that treat decomposition as a monolithic process, StructuralDecompose separates the analysis into distinct components: changepoint detection, anomaly detection, smoothing, and decomposition. This design provides flexibility and robust- ness, allowing users to tailor methods to specific time series characteristics. We demonstrate the package on simulated and real-world datasets, benchmark its performance against state-of-the- art tools such as Rbeast and autostsm, and discuss its role in interpretable machine learning workflows.
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