Identification of Switched Models in Non-Stationary Time Series based on Coordinate-Descent and Genetic Algorithm
M. Gorji, Mina Moradi Kordmahalleh, Abdollah Homaifar · 2015
Time series analysis is an important research topic in science and engineering. Real-world time series are usually non-stationary with time-varying parameters. Identification of non-stationary time series with a switched model includes finding the switch times and model parameters in each cluster. This problem is a non-convex optimization with equality constraints. Conventional identification methods suffer from restrictive statistical assumptions about the data or switch times, locality of solution, and computational complexity particularly for longer time series. In this paper, a novel coordinate-descent algorithm with the genetic algorithm (GA) and statistical inference is developed. In the evolutionary process, innovative types of crossover and mutation are proposed to improve exploration and exploitation capabilities of the GA, and fitness of the individuals are calculated by the maximum likelihood or least-mean-square.