StructuralDecompose: A Modular Framework for Robust Time Series Decomposition in R
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915535218802688 |
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| author | Sunny, Allen Daniel |
| author_facet | Sunny, Allen Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04974 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | StructuralDecompose: A Modular Framework for Robust Time Series Decomposition in R Sunny, Allen Daniel Machine Learning 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. |
| title | StructuralDecompose: A Modular Framework for Robust Time Series Decomposition in R |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.04974 |