Authors:
Alexei Bocharov
and
Bo Thiesson
Affiliation:
Microsoft Research, United States
Keyword(s):
Regime-switching time series, Spectral clustering, Regression tree, Oblique split, Financial markets.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Economics, Business and Forecasting Applications
;
Model Selection
;
Pattern Recognition
;
Regression
;
Spectral Methods
;
Theory and Methods
Abstract:
We introduce a non-parametric approach for the segmentation in regime-switching time-series models. The
approach is based on spectral clustering of target-regressor tuples and derives a switching regression tree,
where regime switches are modeled by oblique splits. Our segmentation method is very parsimonious in the
number of splits evaluated during the construction process of the tree–for a candidate node, the method only
proposes one oblique split on regressors and a few targeted splits on time. The regime-switching model can
therefore be learned efficiently from data. We use the class of ART time series models to serve as illustration,
but because of the non-parametric nature of our segmentation approach, it readily generalizes to a wide range
of time-series models that go beyond the Gaussian error assumption in ART models. Experimental results on
S&P 1500 financial trading data demonstrates dramatically improved predictive accuracy for the exemplifying
ART models.