Time Series Classification and its Applications

Time-series classification is one of the most important machine learning tasks related to time series. It is the common denominator in various recognition tasks, such as signature verification, person identification based on keystroke dynamics, detection of cardiovascular diseases and brain disorders (e.g. early stage of Alzheimer disease or dementia). This tutorial aims to give an introduction to the most prominent challenges, methods, evaluation protocols and biomedical applications related to time series classification. Besides the "conventional" time series classification task, early classification and semi-supervised classification will be considered. Both preprocessing techniques -- FFT, SAX, etc. -- and wide-spread classifiers -- such as similarity-based, feature-based, motif/shaplet-based classifiers and convolutional neural networks -- will be covered. As dynamic time warping (DTW) is the one of the key components of many time series classifiers, including recent ones based on deep learning, we will describe this technique in detail. Slides: http://www.biointelligence.hu/pdf/timeseriestutorial.pdf

[1]  Kristóf Marussy,et al.  SUCCESS: A New Approach for Semi-supervised Classification of Time-Series , 2013, ICAISC.

[2]  Krisztian Buza,et al.  Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture , 2017, Front. Neuroinform..

[3]  George Manis,et al.  Heartbeat Time Series Classification With Support Vector Machines , 2009, IEEE Transactions on Information Technology in Biomedicine.

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

[5]  Noémi Gaskó,et al.  Feature Selection with a Genetic Algorithm for Classification of Brain Imaging Data , 2018, Advances in Feature Selection for Data and Pattern Recognition.

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

[7]  José Manuel Benítez,et al.  Self-labeling techniques for semi-supervised time series classification: an empirical study , 2018, Knowledge and Information Systems.

[8]  Lars Schmidt-Thieme,et al.  Fast Classification of Electrocardiograph Signals via Instance Selection , 2011, 2011 IEEE First International Conference on Healthcare Informatics, Imaging and Systems Biology.

[9]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[10]  Chris Chatfield,et al.  Time‐series forecasting , 2000 .

[11]  Kristóf Marussy,et al.  PROCESS: Projection-Based Classification of Electroencephalograph Signals , 2015, ICAISC.

[12]  Yong Duan,et al.  Active Learning for Multivariate Time Series Classification with Positive Unlabeled Data , 2015, 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI).

[13]  Chotirat Ratanamahatana,et al.  Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery , 2015, Comput. Math. Methods Medicine.

[14]  Tutut Herawan,et al.  Computational and mathematical methods in medicine. , 2006, Computational and mathematical methods in medicine.

[15]  T. Warren Liao,et al.  Clustering of time series data - a survey , 2005, Pattern Recognit..

[16]  Krisztian Buza,et al.  Projection-Based Person Identification , 2017, CORES.

[17]  Mirjana Ivanovic,et al.  Time-series mining in a psychological domain , 2012, BCI '12.

[18]  Gabriela Lindemann von Trzebiatowski,et al.  Time-series analysis in the medical domain: A study of Tacrolimus administration and influence on kidney graft function , 2014, Comput. Biol. Medicine.

[19]  Lars Schmidt-Thieme,et al.  Fusion of Similarity Measures for Time Series Classification , 2011, HAIS.

[20]  Lars Schmidt-Thieme,et al.  INSIGHT: Efficient and Effective Instance Selection for Time-Series Classification , 2011, PAKDD.

[21]  Alexandros Nanopoulos,et al.  Time-Series Classification in Many Intrinsic Dimensions , 2010, SDM.

[22]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[23]  Pietro Perona,et al.  Continuous dynamic time warping for translation-invariant curve alignment with applications to signature verification , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[24]  Eamonn J. Keogh,et al.  LB_Keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures , 2006, VLDB.

[25]  Devavrat Shah,et al.  A Latent Source Model for Nonparametric Time Series Classification , 2013, NIPS.

[26]  Lovekesh Vig,et al.  Long Short Term Memory Networks for Anomaly Detection in Time Series , 2015, ESANN.

[27]  Lars Schmidt-Thieme,et al.  Time-Series Classification Based on Individualised Error Prediction , 2010, 2010 13th IEEE International Conference on Computational Science and Engineering.

[28]  Eamonn J. Keogh,et al.  A symbolic representation of time series, with implications for streaming algorithms , 2003, DMKD '03.

[29]  Lars Schmidt-Thieme,et al.  Motif-Based Classification of Time Series with Bayesian Networks and SVMs , 2008, GfKl.

[30]  Mirjana Ivanovic,et al.  Comparison of different weighting schemes for the kNN classifier on time-series data , 2016, Knowledge and Information Systems.

[31]  Mirjana Ivanovic,et al.  A Framework for Time-Series Analysis , 2010, AIMSA.

[32]  G. Lugosi,et al.  On the Strong Universal Consistency of Nearest Neighbor Regression Function Estimates , 1994 .

[33]  Yi Zheng,et al.  Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks , 2014, WAIM.

[34]  Philip S. Yu,et al.  Early classification on time series , 2012, Knowledge and Information Systems.

[35]  Xiaoli Li,et al.  Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition , 2015, IJCAI.