StructuralDecompose: A Modular Framework for Robust Time Series Decomposition in R
Abstract
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.
Cite
@article{arxiv.2510.04974,
title = {StructuralDecompose: A Modular Framework for Robust Time Series Decomposition in R},
author = {Allen Daniel Sunny},
journal= {arXiv preprint arXiv:2510.04974},
year = {2025}
}
Comments
8 pages, 4 figures. Part of the R package StructuralDecompose (https://cran.r-project.org/web/packages/StructuralDecompose/index.html)