Early classification on multivariate time series

Multivariate time series (MTS) classification is an important topic in time series data mining, and has attracted great interest in recent years. However, early classification on MTS data largely remains a challenging problem. To address this problem without sacrificing the classification performance, we focus on discovering hidden knowledge from the data for early classification in an explainable way. At first, we introduce a method MCFEC (Mining Core Feature for Early Classification) to obtain distinctive and early shapelets as core features of each variable independently. Then, two methods are introduced for early classification on MTS based on core features. Experimental results on both synthetic and real-world datasets clearly show that our proposed methods can achieve effective early classification on MTS.

[1]  See-Kiong Ng,et al.  Positive Unlabeled Leaning for Time Series Classification , 2011, IJCAI.

[2]  Eamonn J. Keogh,et al.  Logical-shapelets: an expressive primitive for time series classification , 2011, KDD.

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

[4]  Nikolaos Kourentzes,et al.  Feature selection for time series prediction - A combined filter and wrapper approach for neural networks , 2010, Neurocomputing.

[5]  Cécile Amblard,et al.  Classification trees for time series , 2012, Pattern Recognit..

[6]  Chellu Chandra Sekhar,et al.  Classification of varying length multivariate time series using Gaussian mixture models and support vector machines , 2010, Int. J. Data Min. Model. Manag..

[7]  Cyrus Shahabi,et al.  CL eVer: A Feature Subset Selection Technique for Multivariate Time Series , 2005, PAKDD.

[8]  Cyrus Shahabi,et al.  Feature subset selection and feature ranking for multivariate time series , 2005, IEEE Transactions on Knowledge and Data Engineering.

[9]  Philip S. Yu,et al.  Early prediction on time series: a nearest neighbor approach , 2009, IJCAI 2009.

[10]  Mohamed F. Ghalwash,et al.  Early classification of multivariate temporal observations by extraction of interpretable shapelets , 2012, BMC Bioinformatics.

[11]  Xu Chen,et al.  Early prediction on imbalanced multivariate time series , 2013, CIKM.

[12]  Li Wei,et al.  Fast time series classification using numerosity reduction , 2006, ICML.

[13]  Vit Niennattrakul,et al.  Shape-based template matching for time series data , 2012, Knowl. Based Syst..

[14]  Jason Lines,et al.  Classification of time series by shapelet transformation , 2013, Data Mining and Knowledge Discovery.

[15]  Tak-Chung Fu,et al.  A review on time series data mining , 2011, Eng. Appl. Artif. Intell..

[16]  Junyi Shen,et al.  Classification of multivariate time series using two-dimensional singular value decomposition , 2008, Knowl. Based Syst..

[17]  Milos Hauskrecht,et al.  Mining recent temporal patterns for event detection in multivariate time series data , 2012, KDD.

[18]  Jason Lines,et al.  A shapelet transform for time series classification , 2012, KDD.

[19]  Li Wei,et al.  Semi-supervised time series classification , 2006, KDD '06.

[20]  Milos Hauskrecht,et al.  Multivariate Time Series Classification with Temporal Abstractions , 2009, FLAIRS.

[21]  Minyoung Kim,et al.  Semi-supervised learning of hidden conditional random fields for time-series classification , 2013, Neurocomputing.

[22]  Chunshien Li,et al.  A novel self-organizing complex neuro-fuzzy approach to the problem of time series forecasting , 2013, Neurocomputing.

[23]  Jian Pei,et al.  A brief survey on sequence classification , 2010, SKDD.

[24]  Olufemi A. Omitaomu,et al.  Weighted dynamic time warping for time series classification , 2011, Pattern Recognit..

[25]  Carlo Vercellis,et al.  Combining discrete SVM and fixed cardinality warping distances for multivariate time series classification , 2010, Pattern Recognit..

[26]  H. Sebastian Seung,et al.  Query by committee , 1992, COLT '92.

[27]  H. Sebastian Seung,et al.  Selective Sampling Using the Query by Committee Algorithm , 1997, Machine Learning.

[28]  Hui Ding,et al.  Querying and mining of time series data: experimental comparison of representations and distance measures , 2008, Proc. VLDB Endow..

[29]  Yang Wang,et al.  Cost-sensitive boosting for classification of imbalanced data , 2007, Pattern Recognit..

[30]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[31]  Eamonn J. Keogh,et al.  Time series shapelets: a new primitive for data mining , 2009, KDD.

[32]  Peter Stagge,et al.  Recurrent neural networks for time series classification , 2003, Neurocomputing.

[33]  Philip S. Yu,et al.  Extracting Interpretable Features for Early Classification on Time Series , 2011, SDM.

[34]  Min Han,et al.  Feature selection techniques with class separability for multivariate time series , 2013, Neurocomputing.

[35]  Eamonn J. Keogh,et al.  DTW-D: time series semi-supervised learning from a single example , 2013, KDD.

[36]  Qinghua Hu,et al.  Dynamic time warping constraint learning for large margin nearest neighbor classification , 2011, Inf. Sci..