Detection of Structural Breaks and Outliers in Time Series<br/><br/>

Friday, September 9, 2011 - 9:00am - 10:00am
Keller 3-180
Richard Davis (Columbia University)
We will consider the problem of modeling a class of non-stationary time series with outliers 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 and each piece may be contaminated with an unknown number of innovational and/or additive outliers. The minimum description length principle is applied to compare various segmented AR fits to the data. The goal is to find the “best” combination of the number of segments, the lengths of the segments, the orders of the piecewise AR processes, the number and type of outliers. Such a “best” combination is implicitly defined as the optimizer of a MDL criterion. Since the optimization is carried over a large number of configurations of segments and positions of outliers, a genetic algorithm is used to find optimal or near optimal solutions.

Strategies for accelerating the procedure will also be described. Numerical results from simulation experiments and real data analyses show that the procedure enjoys excellent empirical properties. (This is joint work with Thomas Lee and Gabriel Rodriguez-Yam.)