# Synthesis of Parametric Hybrid Automata from Time Series

arXiv: https://arxiv.org/abs/2208.06383 · HTML: https://mlsanity.com/paper/2208.06383/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 45 references that are in the map.
- First posted on arXiv 13 Jul 2022.

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

- [LazySets.jl: Scalable Symbolic-Numeric Set Computations](https://mlsanity.com/paper/2110.01711/) (2021)
- [CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis](https://mlsanity.com/paper/1707.03336/) (2017)
- [Synthesis of Hybrid Automata with Affine Dynamics from Time-Series Data](https://mlsanity.com/paper/2102.12734/) (2021)

## Abstract (arXiv)

We propose an algorithmic approach for synthesizing linear hybrid automata from time-series data. Unlike existing approaches, our approach provides a whole family of models. Each model in the family is guaranteed to capture the input data up to a precision error ε, in the following sense: For each time series, the model contains an execution that is ε-close to the data points. Our construction allows to effectively choose a model from this family with minimal precision error ε. We demonstrate the algorithm's efficiency and its ability to find precise models in two case studies.