![](https://dblp.dagstuhl.de/img/logo.ua.320x120.png)
![](https://dblp.dagstuhl.de/img/dropdown.dark.16x16.png)
![](https://dblp.dagstuhl.de/img/peace.dark.16x16.png)
Остановите войну!
for scientists:
![search dblp search dblp](https://dblp.dagstuhl.de/img/search.dark.16x16.png)
![search dblp](https://dblp.dagstuhl.de/img/search.dark.16x16.png)
default search action
Anthony J. Bagnall
Person information
- affiliation: University of East Anglia, School of Computing Sciences, Norwich, UK
Refine list
![note](https://dblp.dagstuhl.de/img/note-mark.dark.12x12.png)
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
Journal Articles
- 2024
- [j30]Matthew Middlehurst, Patrick Schäfer, Anthony J. Bagnall:
Bake off redux: a review and experimental evaluation of recent time series classification algorithms. Data Min. Knowl. Discov. 38(4): 1958-2031 (2024) - [j29]David Guijo-Rubio, Matthew Middlehurst, Guilherme Arcencio, Diego Furtado Silva, Anthony J. Bagnall:
Unsupervised feature based algorithms for time series extrinsic regression. Data Min. Knowl. Discov. 38(4): 2141-2185 (2024) - [j28]Christopher Holder
, Matthew Middlehurst
, Anthony J. Bagnall
:
A review and evaluation of elastic distance functions for time series clustering. Knowl. Inf. Syst. 66(2): 765-809 (2024) - 2021
- [j27]Alejandro Pasos Ruiz
, Michael Flynn
, James Large
, Matthew Middlehurst
, Anthony J. Bagnall
:
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Discov. 35(2): 401-449 (2021) - [j26]Matthew Middlehurst
, James Large
, Michael Flynn
, Jason Lines
, Aaron Bostrom
, Anthony J. Bagnall
:
HIVE-COTE 2.0: a new meta ensemble for time series classification. Mach. Learn. 110(11): 3211-3243 (2021) - 2020
- [j25]Shaghayegh Gharghabi
, Shima Imani, Anthony J. Bagnall, Amirali Darvishzadeh, Eamonn J. Keogh:
An ultra-fast time series distance measure to allow data mining in more complex real-world deployments. Data Min. Knowl. Discov. 34(4): 1104-1135 (2020) - 2019
- [j24]James Large
, Jason Lines
, Anthony J. Bagnall
:
A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates. Data Min. Knowl. Discov. 33(6): 1674-1709 (2019) - [j23]James Large
, Anthony J. Bagnall, Simon Malinowski, Romain Tavenard:
On time series classification with dictionary-based classifiers. Intell. Data Anal. 23(5): 1073-1089 (2019) - [j22]Hoang Anh Dau, Anthony J. Bagnall, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, Shaghayegh Gharghabi, Chotirat Ann Ratanamahatana, Eamonn J. Keogh:
The UCR time series archive. IEEE CAA J. Autom. Sinica 6(6): 1293-1305 (2019) - 2018
- [j21]Hoang Anh Dau
, Diego Furtado Silva, François Petitjean, Germain Forestier
, Anthony J. Bagnall, Abdullah Mueen, Eamonn J. Keogh:
Optimizing dynamic time warping's window width for time series data mining applications. Data Min. Knowl. Discov. 32(4): 1074-1120 (2018) - [j20]Jason Lines
, Sarah Taylor, Anthony J. Bagnall:
Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles. ACM Trans. Knowl. Discov. Data 12(5): 52:1-52:35 (2018) - 2017
- [j19]Anthony J. Bagnall
, Jason Lines
, Aaron Bostrom, James Large
, Eamonn J. Keogh:
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Discov. 31(3): 606-660 (2017) - [j18]Aaron Bostrom, Anthony J. Bagnall:
Binary Shapelet Transform for Multiclass Time Series Classification. Trans. Large Scale Data Knowl. Centered Syst. 32: 24-46 (2017) - 2016
- [j17]Reda Younsi
, Anthony J. Bagnall:
Ensembles of random sphere cover classifiers. Pattern Recognit. 49: 213-225 (2016) - 2015
- [j16]Jason Lines
, Anthony J. Bagnall:
Time series classification with ensembles of elastic distance measures. Data Min. Knowl. Discov. 29(3): 565-592 (2015) - [j15]Anthony J. Bagnall, Jason Lines
, Jon Hills, Aaron Bostrom:
Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles. IEEE Trans. Knowl. Data Eng. 27(9): 2522-2535 (2015) - 2014
- [j14]Anthony J. Bagnall, Gareth J. Janacek:
A Run Length Transformation for Discriminating Between Auto Regressive Time Series. J. Classif. 31(2): 154-178 (2014) - [j13]Jon Hills, Jason Lines
, Edgaras Baranauskas, James Mapp, Anthony J. Bagnall:
Classification of time series by shapelet transformation. Data Min. Knowl. Discov. 28(4): 851-881 (2014) - 2013
- [j12]Jon Hills, Anthony J. Bagnall, Beatriz de la Iglesia
, Graeme Richards:
BruteSuppression: a size reduction method for Apriori rule sets. J. Intell. Inf. Syst. 40(3): 431-454 (2013) - 2012
- [j11]Reda Younsi
, Anthony J. Bagnall:
An efficient randomised sphere cover classifier. Int. J. Data Min. Model. Manag. 4(2): 156-171 (2012) - [j10]Luke M. Davis, Barry-John Theobald, Jason Lines
, Andoni Toms, Anthony J. Bagnall:
On the Segmentation and Classification of Hand Radiographs. Int. J. Neural Syst. 22(5) (2012) - 2009
- [j9]Zhanna V. Zatuchna, Anthony J. Bagnall:
Learning Mazes with Aliasing States: An LCS Algorithm with Associative Perception. Adapt. Behav. 17(1): 28-57 (2009) - [j8]Zhanna V. Zatuchna, Anthony J. Bagnall:
A learning classifier system for mazes with aliasing clones. Nat. Comput. 8(1): 57-99 (2009) - 2007
- [j7]Anthony J. Bagnall, Gavin C. Cawley, Ian M. Whittley, Larry Bull, Matthew Studley, Mike Pettipher, Firat Tekiner:
Super Computer Heterogeneous Classifier Meta-Ensembles. Int. J. Data Warehous. Min. 3(2): 67-82 (2007) - [j6]Larry Bull, Matthew Studley, Anthony J. Bagnall, Ian M. Whittley:
Learning Classifier System Ensembles With Rule-Sharing. IEEE Trans. Evol. Comput. 11(4): 496-502 (2007) - 2006
- [j5]Anthony J. Bagnall, Iain Toft:
Autonomous Adaptive Agents for Single Seller Sealed Bid Auctions. Auton. Agents Multi Agent Syst. 12(3): 259-292 (2006) - [j4]Anthony J. Bagnall, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh, Stefano Lonardi
, Gareth J. Janacek:
A Bit Level Representation for Time Series Data Mining with Shape Based Similarity. Data Min. Knowl. Discov. 13(1): 11-40 (2006) - 2005
- [j3]Anthony J. Bagnall, Gareth J. Janacek:
Clustering Time Series with Clipped Data. Mach. Learn. 58(2-3): 151-178 (2005) - [j2]Anthony J. Bagnall, George D. Smith:
A multiagent model of the UK market in electricity generation. IEEE Trans. Evol. Comput. 9(5): 522-536 (2005) - 2001
- [j1]Anthony J. Bagnall, Victor J. Rayward-Smith, Ian M. Whittley:
The next release problem. Inf. Softw. Technol. 43(14): 883-890 (2001)
Conference and Workshop Papers
- 2024
- [c51]Anthony J. Bagnall, Matthew Middlehurst, Germain Forestier, Ali Ismail-Fawaz, Antoine Guillaume, David Guijo-Rubio, Chang Wei Tan, Angus Dempster, Geoffrey I. Webb:
A Hands-on Introduction to Time Series Classification and Regression. KDD 2024: 6410-6411 - 2023
- [c50]Arik Ermshaus
, Patrick Schäfer, Anthony J. Bagnall, Thomas Guyet, Georgiana Ifrim, Vincent Lemaire, Ulf Leser, Colin Leverger, Simon Malinowski:
Human Activity Segmentation Challenge @ ECML/PKDD'23. AALTD@ECML/PKDD 2023: 3-13 - [c49]Christopher Holder, David Guijo-Rubio, Anthony J. Bagnall:
Clustering Time Series with k-Medoids Based Algorithms. AALTD@ECML/PKDD 2023: 39-55 - [c48]Matthew Middlehurst, Anthony J. Bagnall:
Extracting Features from Random Subseries: A Hybrid Pipeline for Time Series Classification and Extrinsic Regression. AALTD@ECML/PKDD 2023: 113-126 - [c47]Christopher Holder, David Guijo-Rubio, Anthony J. Bagnall:
Barycentre Averaging for the Move-Split-Merge Time Series Distance Measure. KDIR 2023: 51-62 - [c46]Aiden Rushbrooke, Jordan Tsigarides, Saber Sami, Anthony J. Bagnall:
Time Series Classification of Electroencephalography Data. IWANN (1) 2023: 601-613 - 2022
- [c45]Alejandro Pasos Ruiz, Anthony J. Bagnall:
Dimension Selection Strategies for Multivariate Time Series Classification with HIVE-COTEv2.0. AALTD@ECML/PKDD 2022: 133-147 - [c44]Matthew Middlehurst, Anthony J. Bagnall:
The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier. ICPRAI (2) 2022: 150-161 - 2020
- [c43]Matthew Middlehurst
, James Large, Anthony J. Bagnall:
The Canonical Interval Forest (CIF) Classifier for Time Series Classification. IEEE BigData 2020: 188-195 - [c42]David Guijo-Rubio
, Pedro Antonio Gutiérrez, Anthony J. Bagnall, César Hervás-Martínez:
Time series ordinal classification via shapelets. IJCNN 2020: 1-8 - [c41]Anthony J. Bagnall, Michael Flynn, James Large
, Jason Lines, Matthew Middlehurst
:
On the Usage and Performance of the Hierarchical Vote Collective of Transformation-Based Ensembles Version 1.0 (HIVE-COTE v1.0). AALTD@PKDD/ECML 2020: 3-18 - [c40]David Guijo-Rubio
, Pedro Antonio Gutiérrez
, Anthony J. Bagnall
, César Hervás-Martínez
:
Ordinal Versus Nominal Time Series Classification. AALTD@PKDD/ECML 2020: 19-29 - [c39]Matthew Middlehurst
, James Large
, Gavin C. Cawley, Anthony J. Bagnall:
The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification. ECML/PKDD (1) 2020: 660-676 - [c38]Thakolpat Khampuengson, Anthony J. Bagnall, Wenjia Wang:
Developing Ensemble Methods for Detecting Anomalies in Water Level Data. SGAI Conf. 2020: 145-151 - 2019
- [c37]James Large
, Paul Southam, Anthony J. Bagnall:
Can Automated Smoothing Significantly Improve Benchmark Time Series Classification Algorithms? HAIS 2019: 50-60 - [c36]Matthew Middlehurst
, William Vickers, Anthony J. Bagnall:
Scalable Dictionary Classifiers for Time Series Classification. IDEAL (1) 2019: 11-19 - [c35]James Large
, Anthony J. Bagnall:
Mixing Hetero- and Homogeneous Models in Weighted Ensembles. IDEAL (1) 2019: 129-136 - [c34]David Guijo-Rubio
, Pedro Antonio Gutiérrez
, Romain Tavenard, Anthony J. Bagnall:
A Hybrid Approach to Time Series Classification with Shapelets. IDEAL (1) 2019: 137-144 - [c33]Michael Flynn
, Anthony J. Bagnall:
Classifying Flies Based on Reconstructed Audio Signals. IDEAL (2) 2019: 249-258 - 2018
- [c32]Shaghayegh Gharghabi, Shima Imani, Anthony J. Bagnall, Amirali Darvishzadeh, Eamonn J. Keogh:
Matrix Profile XII: MPdist: A Novel Time Series Distance Measure to Allow Data Mining in More Challenging Scenarios. ICDM 2018: 965-970 - [c31]James Large
, E. Kate Kemsley
, Nikolaus Wellner, Ian Goodall, Anthony J. Bagnall:
Detecting Forged Alcohol Non-invasively Through Vibrational Spectroscopy and Machine Learning. PAKDD (1) 2018: 298-309 - 2017
- [c30]Hoang Anh Dau, Diego Furtado Silva, François Petitjean, Germain Forestier
, Anthony J. Bagnall, Eamonn J. Keogh:
Judicious setting of Dynamic Time Warping's window width allows more accurate classification of time series. IEEE BigData 2017: 917-922 - 2016
- [c29]Anthony J. Bagnall, Jason Lines
, Jon Hills, Aaron Bostrom:
Time-series classification with COTE: The collective of transformation-based ensembles. ICDE 2016: 1548-1549 - [c28]Jason Lines
, Sarah Taylor, Anthony J. Bagnall:
HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles for Time Series Classification. ICDM 2016: 1041-1046 - 2015
- [c27]Aaron Bostrom, Anthony J. Bagnall:
Binary Shapelet Transform for Multiclass Time Series Classification. DaWaK 2015: 257-269 - [c26]Awat A. Saeed, Gavin C. Cawley, Anthony J. Bagnall:
Benchmarking the semi-supervised naïve Bayes classifier. IJCNN 2015: 1-8 - 2014
- [c25]Jason Lines
, Anthony J. Bagnall:
Ensembles of Elastic Distance Measures for Time Series Classification. SDM 2014: 524-532 - 2013
- [c24]James Mapp, Mark Fisher, Anthony J. Bagnall, Jason Lines, Sally Warne, Joe Scutt Phillips:
Clupea Harengus: Intraspecies Distinction using Curvature Scale Space and Shapelets - Classification of North-sea and Thames Herring using Boundary Contour of Sagittal Otoliths. ICPRAM 2013: 138-143 - 2012
- [c23]Luke M. Davis, Barry-John Theobald, Anthony J. Bagnall:
Automated Bone Age Assessment Using Feature Extraction. IDEAL 2012: 43-51 - [c22]Jon Hills, Luke M. Davis, Anthony J. Bagnall:
Interestingness Measures for Fixed Consequent Rules. IDEAL 2012: 68-75 - [c21]Jason Lines
, Anthony J. Bagnall:
Alternative Quality Measures for Time Series Shapelets. IDEAL 2012: 475-483 - [c20]Jason Lines
, Luke M. Davis, Jon Hills, Anthony J. Bagnall:
A shapelet transform for time series classification. KDD 2012: 289-297 - [c19]Anthony J. Bagnall, Luke M. Davis, Jon Hills, Jason Lines:
Transformation Based Ensembles for Time Series Classification. SDM 2012: 307-318 - 2011
- [c18]Luke M. Davis, Barry-John Theobald, Andoni Toms, Anthony J. Bagnall:
On the Extraction and Classification of Hand Outlines. IDEAL 2011: 92-99 - [c17]Jason Lines
, Anthony J. Bagnall, Patrick Caiger-Smith, Simon Anderson:
Classification of Household Devices by Electricity Usage Profiles. IDEAL 2011: 403-412 - 2010
- [c16]Reda Younsi
, Anthony J. Bagnall:
A Randomized Sphere Cover Classifier. IDEAL 2010: 234-241 - 2008
- [c15]Anthony J. Bagnall, Simon Moxon, David J. Studholme, Vincent Moulton:
Time Series Data Mining Algorithms for Identifying Short RNA in Arabidopsis thaliana. BIOCOMP 2008: 182-188 - 2007
- [c14]Iain Toft, Anthony J. Bagnall:
Adaptive Sniping for Volatile and Stable Continuous Double Auction Markets. AMEC/TADA 2007: 119-134 - 2006
- [c13]Anthony J. Bagnall, Ian M. Whittley, Gareth J. Janacek, Kate Kemsley, Matthew Studley, Larry Bull:
A Comparison of DWT/PAA and DFT for Time Series Classification. DMIN 2006: 403-409 - [c12]Anthony J. Bagnall, Ian M. Whittley, Matthew Studley, Mike Pettipher, Firat Tekiner, Larry Bull:
Variance Stabilizing Regression Ensembles for Environmental Models. IJCNN 2006: 5355-5361 - 2005
- [c11]Larry Bull, Matthew Studley, Anthony J. Bagnall, Ian M. Whittley:
On the use of rule-sharing in learning classifier system ensembles. Congress on Evolutionary Computation 2005: 612-617 - [c10]Zhanna V. Zatuchna, Anthony J. Bagnall:
AgentP classifier system: self-adjusting vs. gradual approach. Congress on Evolutionary Computation 2005: 1196-1203 - [c9]Gareth J. Janacek, Anthony J. Bagnall, M. Powell:
A Likelihood Ratio Distance Measure for the Similarity Between the Fourier Transform of Time Series. PAKDD 2005: 737-743 - [c8]Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh, Anthony J. Bagnall, Stefano Lonardi:
A Novel Bit Level Time Series Representation with Implication of Similarity Search and Clustering. PAKDD 2005: 771-777 - 2004
- [c7]Abdul Aziz Gill, George D. Smith, Anthony J. Bagnall:
Improving Decision Tree Performance Through Induction- and Cluster-Based Stratified Sampling. IDEAL 2004: 339-344 - [c6]Anthony J. Bagnall, Gareth J. Janacek:
Clustering time series from ARMA models with clipped data. KDD 2004: 49-58 - 2003
- [c5]Anthony J. Bagnall, Iain Toft:
An Agent Model for First Price and Second Price Private Value Auctions. Artificial Evolution 2003: 281-292 - [c4]Beatriz de la Iglesia
, M. S. Philpott, Anthony J. Bagnall, Victor J. Rayward-Smith:
Data mining rules using multi-objective evolutionary algorithms. IEEE Congress on Evolutionary Computation 2003: 1552-1559 - 2000
- [c3]Anthony J. Bagnall:
A Multi-Adaptive Agent Model of Generator Bidding in the UK Market in Electricity. GECCO 2000: 605-612 - 1999
- [c2]Anthony J. Bagnall, George D. Smith:
Using an Adaptive Agent to Bid in a Simplified Model of the UK Market in Electricity. GECCO 1999: 774 - 1997
- [c1]Anthony J. Bagnall, Geoff P. McKeown, Victor J. Rayward-Smith:
The Cryptanalysis of a Three Rotor Machine Using a Genetic Algorithm. ICGA 1997: 712-718
Editorship
- 2023
- [e5]Thomas Guyet
, Georgiana Ifrim
, Simon Malinowski
, Anthony J. Bagnall
, Patrick Schäfer
, Vincent Lemaire
:
Advanced Analytics and Learning on Temporal Data - 7th ECML PKDD Workshop, AALTD 2022, Grenoble, France, September 19-23, 2022, Revised Selected Papers. Lecture Notes in Computer Science 13812, Springer 2023, ISBN 978-3-031-24377-6 [contents] - [e4]Georgiana Ifrim
, Romain Tavenard
, Anthony J. Bagnall
, Patrick Schäfer
, Simon Malinowski
, Thomas Guyet
, Vincent Lemaire
:
Advanced Analytics and Learning on Temporal Data - 8th ECML PKDD Workshop, AALTD 2023, Turin, Italy, September 18-22, 2023, Revised Selected Papers. Lecture Notes in Computer Science 14343, Springer 2023, ISBN 978-3-031-49895-4 [contents] - 2021
- [e3]Vincent Lemaire
, Simon Malinowski
, Anthony J. Bagnall
, Thomas Guyet
, Romain Tavenard
, Georgiana Ifrim
:
Advanced Analytics and Learning on Temporal Data - 6th ECML PKDD Workshop, AALTD 2021, Bilbao, Spain, September 13, 2021, Revised Selected Papers. Lecture Notes in Computer Science 13114, Springer 2021, ISBN 978-3-030-91444-8 [contents] - 2020
- [e2]Vincent Lemaire
, Simon Malinowski, Anthony J. Bagnall
, Alexis Bondu, Thomas Guyet
, Romain Tavenard:
Advanced Analytics and Learning on Temporal Data - 4th ECML PKDD Workshop, AALTD 2019, Würzburg, Germany, September 20, 2019, Revised Selected Papers. Lecture Notes in Computer Science 11986, Springer 2020, ISBN 978-3-030-39097-6 [contents] - [e1]Vincent Lemaire
, Simon Malinowski
, Anthony J. Bagnall
, Thomas Guyet
, Romain Tavenard
, Georgiana Ifrim
:
Advanced Analytics and Learning on Temporal Data - 5th ECML PKDD Workshop, AALTD 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers. Lecture Notes in Computer Science 12588, Springer 2020, ISBN 978-3-030-65741-3 [contents]
Informal and Other Publications
- 2024
- [i31]Matthew Middlehurst, Ali Ismail-Fawaz, Antoine Guillaume, Christopher Holder, David Guijo-Rubio, Guzal Bulatova, Leonidas Tsaprounis, Lukasz Mentel, Martin Walter, Patrick Schäfer, Anthony J. Bagnall:
aeon: a Python toolkit for learning from time series. CoRR abs/2406.14231 (2024) - 2023
- [i30]Matthew Middlehurst, Patrick Schäfer, Anthony J. Bagnall:
Bake off redux: a review and experimental evaluation of recent time series classification algorithms. CoRR abs/2304.13029 (2023) - [i29]David Guijo-Rubio, Matthew Middlehurst, Guilherme Arcencio, Diego Furtado Silva, Anthony J. Bagnall:
Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression. CoRR abs/2305.01429 (2023) - [i28]Rafael Ayllón-Gavilán, David Guijo-Rubio, Pedro Antonio Gutiérrez, Anthony J. Bagnall, César Hervás-Martínez:
Convolutional and Deep Learning based techniques for Time Series Ordinal Classification. CoRR abs/2306.10084 (2023) - 2022
- [i27]Matthew Middlehurst, Anthony J. Bagnall:
The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier. CoRR abs/2201.12048 (2022) - [i26]Christopher Holder, Matthew Middlehurst, Anthony J. Bagnall:
A Review and Evaluation of Elastic Distance Functions for Time Series Clustering. CoRR abs/2205.15181 (2022) - 2021
- [i25]Matthew Middlehurst, James Large, Michael Flynn, Jason Lines, Aaron Bostrom, Anthony J. Bagnall:
HIVE-COTE 2.0: a new meta ensemble for time series classification. CoRR abs/2104.07551 (2021) - [i24]Matthew Middlehurst, James Large, Gavin C. Cawley, Anthony J. Bagnall:
The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification. CoRR abs/2105.03841 (2021) - 2020
- [i23]Anthony J. Bagnall, Michael Flynn, James Large, Jason Lines, Matthew Middlehurst:
A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0. CoRR abs/2004.06069 (2020) - [i22]Anthony J. Bagnall, Paul Southam, James Large, Richard W. Harvey:
Detecting Electric Devices in 3D Images of Bags. CoRR abs/2005.02163 (2020) - [i21]Alejandro Pasos Ruiz, Michael Flynn, Anthony J. Bagnall:
Benchmarking Multivariate Time Series Classification Algorithms. CoRR abs/2007.13156 (2020) - [i20]Matthew Middlehurst, James Large, Anthony J. Bagnall:
The Canonical Interval Forest (CIF) Classifier for Time Series Classification. CoRR abs/2008.09172 (2020) - 2019
- [i19]Matthew Middlehurst, William Vickers, Anthony J. Bagnall:
Scalable Dictionary Classifiers for Time Series Classification. CoRR abs/1907.11815 (2019) - [i18]Anthony J. Bagnall, Franz J. Király, Markus Löning, Matthew Middlehurst, George Oastler:
A tale of two toolkits, report the first: benchmarking time series classification algorithms for correctness and efficiency. CoRR abs/1909.05738 (2019) - [i17]Markus Löning, Anthony J. Bagnall, Sajaysurya Ganesh, Viktor Kazakov, Jason Lines, Franz J. Király:
sktime: A Unified Interface for Machine Learning with Time Series. CoRR abs/1909.07872 (2019) - [i16]Anthony J. Bagnall, James Large, Matthew Middlehurst:
A tale of two toolkits, report the second: bake off redux. Chapter 1. dictionary based classifiers. CoRR abs/1911.12008 (2019) - [i15]Anthony J. Bagnall, Richard L. Cole, Themis Palpanas, Konstantinos Zoumpatianos:
Data Series Management (Dagstuhl Seminar 19282). Dagstuhl Reports 9(7): 24-39 (2019) - 2018
- [i14]Anthony J. Bagnall, Aaron Bostrom, Gavin C. Cawley, Michael Flynn, James Large, Jason Lines:
Is rotation forest the best classifier for problems with continuous features? CoRR abs/1809.06705 (2018) - [i13]James Large, Anthony J. Bagnall, Simon Malinowski, Romain Tavenard:
From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers. CoRR abs/1809.06751 (2018) - [i12]Hoang Anh Dau, Anthony J. Bagnall, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, Shaghayegh Gharghabi, Chotirat Ann Ratanamahatana, Eamonn J. Keogh:
The UCR Time Series Archive. CoRR abs/1810.07758 (2018) - [i11]Anthony J. Bagnall, Hoang Anh Dau, Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, Eamonn J. Keogh:
The UEA multivariate time series classification archive, 2018. CoRR abs/1811.00075 (2018) - [i10]James Large, Paul Southam, Anthony J. Bagnall:
Can automated smoothing significantly improve benchmark time series classification algorithms? CoRR abs/1811.00894 (2018) - 2017
- [i9]Anthony J. Bagnall, Gavin C. Cawley:
On the Use of Default Parameter Settings in the Empirical Evaluation of Classification Algorithms. CoRR abs/1703.06777 (2017) - [i8]Anthony J. Bagnall, Aaron Bostrom, James Large, Jason Lines:
Simulated Data Experiments for Time Series Classification Part 1: Accuracy Comparison with Default Settings. CoRR abs/1703.09480 (2017) - [i7]James Large, Jason Lines, Anthony J. Bagnall:
The Heterogeneous Ensembles of Standard Classification Algorithms (HESCA): the Whole is Greater than the Sum of its Parts. CoRR abs/1710.09220 (2017) - [i6]Aaron Bostrom, Anthony J. Bagnall:
A Shapelet Transform for Multivariate Time Series Classification. CoRR abs/1712.06428 (2017) - 2016
- [i5]Anthony J. Bagnall, Aaron Bostrom, James Large, Jason Lines:
The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version. CoRR abs/1602.01711 (2016) - 2014
- [i4]Anthony J. Bagnall, Jason Lines:
An Experimental Evaluation of Nearest Neighbour Time Series Classification. CoRR abs/1406.4757 (2014) - [i3]Anthony J. Bagnall, Luke M. Davis:
Predictive Modelling of Bone Age through Classification and Regression of Bone Shapes. CoRR abs/1406.4781 (2014) - [i2]Anthony J. Bagnall, Jon Hills, Jason Lines:
Finding Motif Sets in Time Series. CoRR abs/1407.3685 (2014) - [i1]Anthony J. Bagnall, Reda Younsi:
Ensembles of Random Sphere Cover Classifiers. CoRR abs/1409.4936 (2014)
Coauthor Index
![](https://dblp.dagstuhl.de/img/cog.dark.24x24.png)
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-08-28 22:31 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint