# Fitting Jump Models

arXiv: https://arxiv.org/abs/1711.09220 · HTML: https://mlsanity.com/paper/1711.09220/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2017.

## Built on by (most cited first, 9 of 9)

- [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)
- [Explainable data-driven modeling via mixture of experts: towards effective blending of grey and black-box models](https://mlsanity.com/paper/2401.17118/) (2024)
- [Robust State-Conditional Feature-Weighted Jump Models for Temporal Clustering](https://mlsanity.com/paper/2606.13146/) (2026)
- [Hybrid Physics-AI Framework of Body Center of Mass Dynamics from Wrist-Worn Sensors](https://mlsanity.com/paper/2609.12304/) (2026)
- [Towards systematic intraday news screening: a liquidity-focused approach](https://mlsanity.com/paper/2304.05115/) (2023)
- [SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study](https://mlsanity.com/paper/2403.00578/) (2024)
- [Real-time Hybrid System Identification with Online Deterministic Annealing](https://mlsanity.com/paper/2408.01730/) (2024)
- [Fuzzy network jump models for soft dynamic clustering of graph-structured data](https://mlsanity.com/paper/2608.05786/) (2026)

## Abstract (arXiv)

We describe a new framework for fitting jump models to a sequence of data. The key idea is to alternate between minimizing a loss function to fit multiple model parameters, and minimizing a discrete loss function to determine which set of model parameters is active at each data point. The framework is quite general and encompasses popular classes of models, such as hidden Markov models and piecewise affine models. The shape of the chosen loss functions to minimize determine the shape of the resulting jump model.