# Robust State-Conditional Feature-Weighted Jump Models for Temporal Clustering

arXiv: https://arxiv.org/abs/2606.13146 · HTML: https://mlsanity.com/paper/2606.13146/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 11 Jun 2026.

## Builds on (most cited first, 3 of 3)

- [Fitting Jump Models](https://mlsanity.com/paper/1711.09220/) (2017)
- [Statistical Jump Model for Mixed-Type Data with Missing Data Imputation](https://mlsanity.com/paper/2409.01208/) (2024)
- [Spatio-Temporal Jump Model for Urban Thermal Comfort Monitoring](https://mlsanity.com/paper/2411.09726/) (2024)

## Built on by (most cited first, 1 of 1)

- [Fuzzy network jump models for soft dynamic clustering of graph-structured data](https://mlsanity.com/paper/2608.05786/) (2026)

## Abstract (arXiv)

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.