Parameter Estimation and Change Detection in Linear Regression Models Using Mixed Integer Linear Programming

We present a method for change detection and parameter estimation in change in the mean models and AR(X) model. The method is based on the assumption of piecewise constant parameters resulting in a sparse structure of their derivative. To illustrate the algorithm and the performance of it, we apply it to the change in the mean model and compare it with four other change detection algorithms. Two applications are treated with good results, fuel monitoring and airbag control. The AR(X) change model, shows the good performance of this method in two illustrative examples.