PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Kernel Methods for Detecting the Direction of Time Series
Jonas Peters, Dominik Janzing and Arthur Gretton
Proccedings of the 32nd Annual Conference of the German Classification Society (GfKl 2008) Volume 01, pp. 1-10, 2009.

Abstract

We propose two kernel based methods for detecting the time direction in empirical time series. First we apply a Support Vector Machine on the finitedimensional distributions of the time series (classification method) by embedding these distributions into a Reproducing Kernel Hilbert Space. For the ARMA method we fit the observed data with an autoregressive moving average process and test whether the regression residuals are statistically independent of the past values. Whenever the dependence in one direction is significantly weaker than in the other we infer the former to be the true one. Both approaches were able to detect the direction of the true generating model for simulated data sets. We also applied our tests to a large number of real world time series. The ARMA method made a decision for a significant fraction of them, in which it was mostly correct, while the classification method did not perform as well, but still exceeded chance level.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:5376
Deposited By:Dominik Janzing
Deposited On:31 March 2009