Structural breaks estimation for non-stationary time series signals

• Published in: IEEE/SP 13th Workshop on Statistical Signal Processing, 2005 pp. 233 - 238
• Main Authors: Davis, R.A., Lee, T.C.M., Rodriguez-Yam, G.A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Analytical models; Data analysis; Frequency; Genetic algorithms; Maximum likelihood estimation; Signal processing; Statistics
• ISBN: 9780780394032; 0780394038
• ISSN: 2373-0803; 2693-3551
• DOI: 10.1109/SSP.2005.1628598

In this work we consider the problem of modeling a class of non-stationary time series signals using piecewise autoregressive (AR) processes. The number and locations of the piecewise autoregressive segments, as well as the orders of the respective AR processes, are assumed to be unknown. The minimum description length principle is applied to find the "best" combination of the number of the segments, the lengths of the segments, and the orders of the piecewise AR processes. A genetic algorithm is implemented to solve this difficult optimization problem. We term the resulting procedure auto-PARM. Numerical results from both simulation experiments and real data analysis show that auto-PARM enjoys excellent empirical properties. Consistency of auto-PARM for break point estimation can also be shown