Robust State-Conditional Feature-Weighted Jump Models for Temporal Clustering
For researchers analyzing time-dependent clustering with outliers, this method provides a more robust and interpretable approach by assigning state-specific feature weights.
The paper proposes a robust feature-weighted jump model for temporal clustering that uses a penalty for smooth transitions and Tukey's biweight loss for robustness. The method outperforms competing approaches in simulations, especially with outliers, and is applied to conflict-related homicides and macroeconomic data.
We propose a robust feature-weighted jump model for time-dependent clustering. A penalty is used to encourage smoothness of transitions over time, while robustness is achieved through the use of a Tukey's biweight loss function. An additional parameter controls the variability of feature weights across states, allowing the model to assign state-specific relevance to each feature. We illustrate in simulation how the method accurately recovers the true cluster sequence and reliably identifies relevant features, outperforming competing approaches, particularly in the presence of outliers. We conclude with two empirical applications, one on the number of conflict-related homicides in Kosovo in the period 1998-2000, and another on macroeconomic performance of twelve European countries in the period 1949-2024.