# StructuralDecompose: A Modular Framework for Robust Time Series Decomposition in R

arXiv: https://arxiv.org/abs/2510.04974 · HTML: https://mlsanity.com/paper/2510.04974/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [Anomaly Detection using One-Class Neural Networks](https://mlsanity.com/paper/1802.06360/) (2018)
- [RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series](https://mlsanity.com/paper/1812.01767/) (2018)
- [Effectively Modeling Time Series with Simple Discrete State Spaces](https://mlsanity.com/paper/2303.09489/) (2023)
- [Automatic Anomaly Detection in the Cloud Via Statistical Learning](https://mlsanity.com/paper/1704.07706/) (2017)
- [Network Traffic Anomaly Detection Using Recurrent Neural Networks](https://mlsanity.com/paper/1803.10769/) (2018)
- [An Elementary Introduction to Kalman Filtering](https://mlsanity.com/paper/1710.04055/) (2017)
- [Time Series Using Exponential Smoothing Cells](https://mlsanity.com/paper/1706.02829/) (2017)
- [A Comparative Study of LOWESS and RBF Approximations for Visualization](https://mlsanity.com/paper/1801.00432/) (2018)
- [Detection of collective and point anomalies in the presence of trend and seasonality](https://mlsanity.com/paper/2508.21128/) (2025)

## Abstract (arXiv)

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.