Classification trees for time series

This paper proposes an extension of classification trees to time series input variables. A new split criterion based on time series proximities is introduced. First, the criterion relies on an adaptive (i.e., parameterized) time series metric to cover both behaviors and values proximities. The metrics parameters may change from one internal node to another to achieve the best bisection of the set of time series. Second, the criterion involves the automatic extraction of the most discriminating subsequences. The proposed time series classification tree is applied to a wide range of datasets: public and new, real and synthetic, univariate and multivariate data. We show, through the experiments performed in this study, that the proposed tree outperforms temporal trees using standard time series distances and performs well compared to other competitive time series classifiers.

[1]  Dennis J. McFarland,et al.  Classification of evoked potentials by Pearson's correlation in a brain-computer interface , 2007 .

[2]  Hans Knutsson,et al.  Robust correlation analysis with an application to functional MRI , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  Jorge Caiado,et al.  A periodogram-based metric for time series classification , 2006, Comput. Stat. Data Anal..

[4]  Shyamal D Peddada,et al.  A random-periods model for expression of cell-cycle genes. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Henrik Boström,et al.  Boosting interval based literals , 2001, Intell. Data Anal..

[6]  Robert H. Shumway,et al.  Discrimination and Clustering for Multivariate Time Series , 1998 .

[7]  Luis Angel García-Escudero,et al.  A Proposal for Robust Curve Clustering , 2005, J. Classif..

[8]  Jeff A. Bilmes,et al.  What HMMs Can Do , 2006, IEICE Trans. Inf. Syst..

[9]  Mineichi Kudo,et al.  Multidimensional curve classification using passing-through regions , 1999, Pattern Recognit. Lett..

[10]  Claude Sammut,et al.  Classification of Multivariate Time Series and Structured Data Using Constructive Induction , 2005, Machine Learning.

[11]  Avi Ma'ayan,et al.  GATE: software for the analysis and visualization of high-dimensional time series expression data , 2009, Bioinform..

[12]  Pang-Ning Tan,et al.  An Integrated Framework for Simultaneous Classification and Regression of Time-Series Data , 2010, SDM.

[13]  Pierre Geurts,et al.  Contributions to decision tree induction: bias/variance tradeoff and time series classification , 2002 .

[14]  David Madigan,et al.  Decision Trees for Functional Variables , 2006, Sixth International Conference on Data Mining (ICDM'06).

[15]  L. Wasserman,et al.  CATS , 2005 .

[16]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[17]  Elizabeth Ann Maharaj,et al.  Cluster of Time Series , 2000, J. Classif..

[18]  Ziv Bar-Joseph,et al.  Clustering short time series gene expression data , 2005, ISMB.

[19]  Pierre Geurts,et al.  Pattern Extraction for Time Series Classification , 2001, PKDD.

[20]  R. Coifman,et al.  Local feature extraction and its applications using a library of bases , 1994 .

[21]  Einoshin Suzuki,et al.  Decision-tree Induction from Time-series Data Based on a Standard-example Split Test , 2003, ICML.

[22]  Ahlame Douzal Chouakria,et al.  Adaptive clustering for time series: Application for identifying cell cycle expressed genes , 2009, Comput. Stat. Data Anal..

[23]  Pierre Geurts,et al.  Segment and Combine Approach for Non-parametric Time-Series Classification , 2005, PKDD.