{"id":162267,"date":"2012-02-01T00:00:00","date_gmt":"2012-02-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/non-parametric-segmentation-of-regime-switching-time-series-with-oblique-switching-trees\/"},"modified":"2018-10-16T20:14:44","modified_gmt":"2018-10-17T03:14:44","slug":"non-parametric-segmentation-of-regime-switching-time-series-with-oblique-switching-trees","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/non-parametric-segmentation-of-regime-switching-time-series-with-oblique-switching-trees\/","title":{"rendered":"Non-Parametric Segmentation of Regime-Switching Time Series with Oblique Switching Trees"},"content":{"rendered":"<div class=\"asset-content\">\n<p>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&#8211;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.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>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&#8211;for [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"alexeib","user_id":"30935"},{"type":"user_nicename","value":"thiesson","user_id":"34026"}],"msr_publishername":"SciTePress Digital Library","msr_publisher_other":"","msr_booktitle":"ICPRAM-2012: 1st International Conference on Pattern Recognition Applications and Methods","msr_chapter":"","msr_edition":"ICPRAM-2012: 1st International Conference on Pattern Recognition Applications and Methods","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"(To appear)","msr_page_range_start":"(to 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