Speaker
Description
Multivariate Singular Spectrum Analysis (MSSA) is a nonparametric tool for time series analysis widely used across finance, healthcare, ecology, and engineering. Traditional MSSA depends on singular value decomposition that is highly susceptible to outliers. We introduce a robust version of MSSA, named Robust Diagonalwise Estimation of SSA (RODESSA), that is able to resist both cellwise and casewise outliers. The decomposition step of MSSA is replaced by a robust low-rank approximation of the trajectory matrix that takes its special structure into account. We devise a fast algorithm that decreases the objective function at each iteration. Additionally, an enhanced time series plot is introduced for better outlier visualization. Through extensive Monte Carlo simulations and a practical case study on temperature monitoring in railway vehicles, RODESSA demonstrates superior performance in handling outliers than competing approaches in the literature.
Acknowledgments: The research activity of F. Centofanti was carried out within the MICS (Made in Italy – Circular and Sustainable) Extended Partnership and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.3 – D.D. 1551.11-10-2022, PE00000004). The research activity of B. Palumbo was carried out within the MOST - Sustainable Mobility National Research Center and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4 – D.D. 1033.17-06-2022, CN00000023). This work reflects only the authors’ views and opinions, neither the European Union nor the European Commission can be considered responsible for them.
| Type of presentation | Talk |
|---|---|
| Classification | Both methodology and application |
| Keywords | Casewise and Cellwise outliers; Multivariate time series; Robust statistics. |