3 papers
eess.SY2026
Classifying Transient Regimes in Dynamic Systems through Properties of Spatial Curves and Stochastic Processes: A Data-Driven Approach
Cristian Puerto-Santana, Javier Diaz-Rozo, Carlos Puerto-Santana +1
This article proposes a novel methodology for the classification of transient and stationary regimes in dynamic systems. Several sensor-based solutions for regime classification in…
stat.ML2023
Context-specific kernel-based hidden Markov model for time series analysis
Carlos Puerto-Santana, Concha Bielza, Pedro Larrañaga +1
Traditional hidden Markov models have been a useful tool to understand and model stochastic dynamic data; in the case of non-Gaussian data, models such as mixture of Gaussian hidde…
cs.LG2020
Autoregressive Asymmetric Linear Gaussian Hidden Markov Models
Carlos Puerto-Santana, Pedro Larrañaga, Concha Bielza
In a real life process evolving over time, the relationship between its relevant variables may change. Therefore, it is advantageous to have different inference models for each sta…