Synthesis of Parametric Hybrid Automata from Time Series
This work addresses the challenge of model synthesis for time-series analysis, offering a method to find precise models, though it appears incremental as it builds on existing approaches by providing a family of models.
The authors tackled the problem of synthesizing linear hybrid automata from time-series data by proposing an algorithmic approach that generates a family of models, each guaranteed to capture the input data within a precision error ε, and demonstrated its efficiency and precision in two case studies.
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.