StructuralDecompose: A Modular Framework for Robust Time Series Decomposition in R
This provides a flexible and robust tool for researchers and practitioners in fields requiring interpretable time series analysis, though it is incremental as it builds on existing decomposition concepts.
The authors tackled the problem of time series decomposition by introducing StructuralDecompose, an R package that modularizes the process into distinct components like changepoint detection and anomaly detection, and demonstrated its performance through benchmarks against state-of-the-art tools.
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.