Fitting jump models
Copyright policy )Fitting jump models
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.
Accepted for publication in Automatica
FOS: Computer and information sciences, jump models, Computer Science - Machine Learning, Estimation and detection in stochastic control theory, Piecewise affine models, Software, source code, etc. for problems pertaining to systems and control theory, Applications of statistics in engineering and industry; control charts, model regression, Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control, Jump models, Machine Learning (cs.LG), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Hidden Markov models, Stochastic systems in control theory (general), Mathematics - Optimization and Control, mode estimation, hidden Markov models, Model regression, Mode estimation, Optimization and Control (math.OC), piecewise affine models, Jump processes
FOS: Computer and information sciences, jump models, Computer Science - Machine Learning, Estimation and detection in stochastic control theory, Piecewise affine models, Software, source code, etc. for problems pertaining to systems and control theory, Applications of statistics in engineering and industry; control charts, model regression, Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control, Jump models, Machine Learning (cs.LG), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Hidden Markov models, Stochastic systems in control theory (general), Mathematics - Optimization and Control, mode estimation, hidden Markov models, Model regression, Mode estimation, Optimization and Control (math.OC), piecewise affine models, Jump processes
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