Publications

Click here to download the bibtex version of the bibliography.


Author Year Title Journal/Book Volume Number Pages
Bagnall, A. & Janacek, G. 2014 A run length transformation for discriminating between auto regressive time series Journal of Classification 31 154--178
Bagnall, A.; Lines, J.; Hills, J. & Bostrom, A. 2015 Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles IEEE Transactions on Knowledge and Data Engineering 27 2522--2535
Batista, G.; Keogh, E.; Tataw, O. & deSouza, V. 2014 CID: an efficient complexity-invariant distance measure for time series Data Mining and Knowledge Discovery 28 3 634--669
Baydogan, M. & Runger, G. 2015 Time series representation and similarity based on local autopatterns Data Mining and Knowledge Discovery
Baydogan, M.; Runger, G. & Tuv, E. 2013 A Bag-of-Features Framework to Classify Time Series IEEE Transactions on Pattern Analysis and Machine Intelligence 25 11 2796--2802
Bostrom, A. & Bagnall, A. 2017 Binary Shapelet Transform for Multiclass Time Series Classification Transactions on Large-Scale Data and Knowledge Centered Systems 32 24--46
Corduas, M. & Piccolo, D. 2008 Time series clustering and classification by the autoregressive metric Computational Statistics and Data Analysis 52 4 1860--1872
Deng, H.; Runger, G.; Tuv, E. & Vladimir, M. 2013 A time series forest for classification and feature extraction Information Sciences 239 142--153
Ding, H.; Trajcevski, G.; Scheuermann, P.; Wang, X. & Keogh, E. 2008 Querying and Mining of Time Series Data: Experimental Comparison of Representations and Distance Measures Proc. 34th VLDB
Fulcher, B. & Jones, N. 2014 Highly comparative feature-based time-series classification IEEE Transactions on Knowledge and Data Engineering 26 12 3026--3037
Gorecki, T. & Luczak, M. 2013 Using derivatives in time series classification Data Mining and Knowledge Discovery 26 2 310--331
Gorecki, T. & Luczak, M. 2014 Non-isometric transforms in time series classification using DTW Knowledge-Based Systems 61 98--108
Grabocka, J. & Schmidt-Thieme, L. 2014 Invariant time-series factorization Data Mining and Knowledge Discovery 28 5 1455--1479
Grabocka, J.; Schilling, N.; Wistuba, M. & Schmidt-Thieme, L. 2014 Learning Time-Series Shapelets Proc. 20th SIGKDD
Hills, J.; Lines, J.; Baranauskas, E.; Mapp, J. & Bagnall, A. 2014 Classification of time series by shapelet transformation Data Mining and Knowledge Discovery 28 4 851--881
Jeong, Y.; Jeong, M. & Omitaomu, O. 2011 Weighted dynamic time warping for time series classification Pattern Recognition 44 2231--2240
Kate, R. 2015 Using dynamic time warping distances as features for improved time series classification Data Mining and Knowledge Discovery
Keogh, E. & Pazzani, M. 2001 Derivative dynamic time warping Proc. 1st SDM
Lin, J.; Keogh, E.; Li, W. & Lonardi, S. 2007 Experiencing SAX: A novel symbolic representation of time series Data Mining and Knowledge Discovery 15 2 107--144
Lin, J.; Khade, R. & Li, Y. 2012 Rotation-invariant similarity in time series using bag-of-patterns representation Journal of Intelligent Information Systems 39 2 287-315
Lines, J. & Bagnall, A. 2015 Time Series Classification with Ensembles of Elastic Distance Measures Data Mining and Knowledge Discovery 29 565--592
Marteau, P. 2009 Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching IEEE Transactions on Pattern Analysis and Machine Intelligence 31 2 306--318
Mueen, A.; Keogh, E. & Young, N. 2011 Logical-Shapelets: An Expressive Primitive for Time Series Classification Proc. 17th SIGKDD
Rakthanmanon, T. & Keogh, E. 2013 Fast-Shapelets: A Fast Algorithm for Discovering Robust Time Series Shapelets Proc. 13th SDM
Ratanamahatana, C. & Keogh, E. 2005 Three Myths about Dynamic Time Warping Data Mining Proc. 5th SDM
Rath, T. & Manamatha, R. 2003 Word image matching using dynamic time warping Proc. Computer Vision and Pattern Recognition
Rodriguez, J.; Alonso, C. & Maestro, J. 2005 Support Vector Machines of Interval-based Features for Time Series Classification Knowledge-Based Systems 18 171--178
Schafer, P. 2015 The BOSS is concerned with time series classification in the presence of noise Data Mining and Knowledge Discovery 29 6 1505--1530
Senin, P. & Malinchik, S. 2013 SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model Proc. 13th IEEE ICDM
Silva, D.; de Souza, V. & Batista, G. 2013 Time Series Classification Using Compression Distance of Recurrence Plots Proc. 13th IEEE ICDM
Stefan, A.; Athitsos, V. & Das, G. 2013 The Move-Split-Merge Metric for Time Series IEEE Transactions on Knowledge and Data Engineering 25 6 1425--1438
Wang, X.; Mueen, A.; Ding, H.; Trajcevski, G.; Scheuermann, P. & Keogh, E. 2013 Experimental comparison of representation methods and distance measures for time series data Data Mining and Knowledge Discovery 26 2 275--309
Ye, L. & Keogh, E. 2011 Time series shapelets: a novel technique that allows accurate, interpretable and fast classification Data Mining and Knowledge Discovery 22 2 149-182
Baydogan, M. & Runger G. 2016 Time series representation and similarity based on local autopatterns Data Mining and Knowledge Discovery 30 2 476--509
Karlsson, I.; Papapetrou, P. & Bostrom, H. 2016 Generalized random shapelet forests Data Mining and Knowledge Discovery 30 5 1053--1085
Yeh, M.; Zhu, Y.; Ulanova, L.; Begum, N.; Ding, Y.; Dau, H.; Silva, D.; Mueen, A. & Keogh, E. 2018 Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile Data Mining and Knowledge Discovery 32 1 83--123
Lines, J.; Taylor, S. & Bagnall, A. 2018 Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-based Ensembles ACM Transactions Knowledge Discovery from Data 12 5 1-36
Schafer, P. 2016 Scalable time series classification Data Mining and Knowledge Discovery 30 5 1273--1298
Schafer, P. & Leser, U. 2017 Fast and accurate time series classification with WEASEL Proc. of 26th ACM CIKM
Fulcher, B. & Jones, N. 2017 hctsa: A computational framework for automated time-series phenotyping using massive feature extraction Cell Systems 5 5 527--531
Karim, F.; Majumdar, S.; Darabi, H. & Chen, S. 2017 LSTM fully convolutional networks for time series classification IEEE access 6 1662--1669
Bagnall, A.; Lines, J.; Bostrom, A.; Large, J. & Keogh, E. 2017 The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances Data Mining and Knowledge Discovery 31 3 606--660
Tan, C.; Herrman, M.; Forestier, G.; Webb, G. & Petitjean, F. 2018 Efficient search of the best warping window for Dynamic Time Warping Proc. of 18th SDM
Dau, H.; Silva, D.; Petitjean, F.; Forestier, G.; Bagnall, A. & Keogh, E. 2018 Optimizing Dynamic Time Warping's Window Width for Time Series Data Mining Applications Data Mining and Knowledge Discovery 32 4 1074--1120
Large, J.; Bagnall, A.; Malinowski, S. & Tavenard, R. 2019 On Time Series Classification with Dictionary-Based Classifiers Intelligent Data Analysis 23 5
Lucas, B.; Shifaz, A.; Pelletier, C.; O'Neill, L.; Zaidi, N.; Goethals, B; Petitjean, F. & Webb, G. 2019 Proximity Forest: an effective and scalable distance-based classifier for time series Data Mining and Knowledge Discovery 33 3 607--635
Abanda, A.; Mori, U. & Lozano, J. 2019 A review on distance based time series classification Data Mining and Knowledge Discovery 33 2 378--412
Flynn, M.; Large, J. & Bagnall, A. 2019 The Contract Random Interval Spectral Ensemble (c-RISE): The Effect of Contracting a Classifier on Accuracy Proc. of 14th HAIS
Large, J.; Southam, P. & Bagnall, A. 2019 Can Automated Smoothing Significantly Improve Benchmark Time Series Classification Algorithms? Proc. of 14th HAIS
Guijo-Rubio, D.; Gutierrez, P.; Tavenard, R. & Bagnall, A. 2019 A Hybrid Approach to Time Series Classification with Shapelets Proc. of 20th IDEAL
Oastler, G. & Lines, J. 2019 A Significantly Faster Elastic-Ensemble for Time-Series Classification Proc. of 20th IDEAL
Middlehurst, M.; Vickers, W. & Bagnall, A. 2019 Scalable dictionary classifiers for time series classification Proc. of 20th IDEAL
Fawaz, H.; Forestier, G.; Weber, J.; Idoumghar, L. & Muller, P. 2019 Deep learning for time series classification: a review Data Mining and Knowledge Discovery 33 4 917--963
Lubba, C.; Sethi, S.; Knaute, P.; Schultz, S.; Fulcher, B. & Jones, N. 2019 catch22: canonical time-series characteristics Data Mining and Knowledge Discovery 33 6 1821--1852
Fawaz, H.; Lucas, B.; Forestier, G.; Pelletier, C.; Schmidt, D.; Weber, J.; Webb, G.; Idoumghar, L.; Muller, P. & Petitjean, F. 2020 InceptionTime: finding AlexNet for time series classification Data Mining and Knowledge Discovery 34 6 1936--1962
Le Nguyen, T.; Gsponer, S.; Ilie, I.; O'Reilly, M. & Ifrim, G. 2019 Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations Data Mining and Knowledge Discovery 33 4 1183--1222
Shifaz, A.; Pelletier, C.; Petitjean, F. & Webb, G. 2020 Ts-chief: A scalable and accurate forest algorithm for time series classification Data Mining and Knowledge Discovery 1--34
Dempster, A.; Petitjean, F. & Webb, G. 2020 ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels Data Mining and Knowledge Discovery 34 1454--1495
Middlehurst, M.; Large, J. & Bagnall, A. 2020 The Canonical Interval Forest (CIF) Classifier for Time Series Classification Proc. of 8th IEEE BigData
Middlehurst, M.; Large, J.; Cawley, G. & Bagnall, A. 2020 The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification Proc. of 20th ECML-PKDD
Bagnall, A.; Flynn, M.; Large, J.; Lines, J. & Middlehurst, M. 2020 On the usage and performance of HIVE-COTE v1.0 Proc. of 5th AALTD
Cabello, N.; Naghizade, E.; Qi, J. & Kulik, L. 2020 Fast and Accurate Time Series Classification Through Supervised Interval Search Proc. of 20th IEEE ICDM