publication . Article . Preprint . Other literature type . 2019

Selected through highly comparative time-series analysis

Simon R. Schultz; Ben D. Fulcher; Sarab S. Sethi; Nick S. Jones; Carl H Lubba; Philip Knaute;
Open Access
  • Published: 09 Aug 2019 Journal: Data Mining and Knowledge Discovery, volume 33, pages 1,821-1,852 (issn: 1384-5810, eissn: 1573-756X, Copyright policy)
  • Publisher: Springer Science and Business Media LLC
  • Country: United Kingdom
Abstract
Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through systematic comparison across a comprehensive time-series feature library, such as those in the hctsa toolbox. However, this approach is computationally expensive and involves evaluating many similar features, limiting the widespread adoption of feature-based representations of time series for real-world applications. In this work, we introduce a met...
Subjects
free text keywords: Computer Networks and Communications, Information Systems, Computer Science Applications, Computer Science - Information Retrieval, Computer Science - Machine Learning, Statistics - Machine Learning, Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Information Systems, Time series, Classification, Clustering, Time-series features, Artificial Intelligence & Image Processing, 0801 Artificial Intelligence and Image Processing, 0804 Data Format, 0806 Information Systems, Scaling, Dimensionality reduction, Autocorrelation, Computation, Linear scale, Artificial intelligence, business.industry, business, Pattern recognition, Outlier, Feature vector, Cluster analysis, Computer science
Related Organizations
33 references, page 1 of 3

1. Bagnall, A., Davis, L.M., Hills, J., Lines, J.: Transformation based ensembles for time series classification. In: Proceedings of the 2012 SIAM International conference on data mining, pp. 307-318 (2012)

2. Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: 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 (2017). DOI 10.1007/s10618-016-0483-9

3. Bagnall, A., Lines, J., Hills, J., Bostrom, A.: Time-series classification with COTE: The collective of transformation-based ensembles. 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016 27(9), 1548-1549 (2016). DOI 10.1109/ICDE.2016. 7498418

4. Bagnall, A., Lines, J., Vickers, W., Keogh, E.: The UEA & UCR Time Series Classification Repository. URL http://www.timeseriesclassification.com/

5. Bandara, K., Bergmeir, C., Smyl, S.: Forecasting across time series databases using long short-term memory networks on groups of similar series: a clustering approach. arXiv (2017). DOI 10.1002/pdi.718. URL http://arxiv.org/abs/1710.03222

6. Berndt, D., Clifford, J.: Using dynamic time warping to find patterns in time series. In: Workshop on Knowledge Knowledge Discovery in Databases, vol. 398, pp. 359-370 (1994)

7. Biason, A., Pielli, C., Rossi, M., Zanella, A., Zordan, D., Kelly, M., Zorzi, M.: ECCENTRIC: An energy- and context-centric perspective on IoT systems and protocol design. IEEE Access 5, 6894-6908 (2017). DOI 10.1109/ACCESS.2017.2692522

8. Dau, H.A., Bagnall, A., Kamgar, K., Yeh, C.M., Zhu, Y.: UCR time series archive 2018. arXiv (2018)

9. Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in timeseries databases. In: SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on management of data, pp. 419-429 (1994)

10. Fisher, R.A.: Statistical Methods for Research Workers (1925). DOI 52,281-302

11. Fulcher, B.D.: 1000 empirical time series (2017). DOI https://doi.org/10.4225/03/ 59c88e1e51868

12. Fulcher, B.D.: Feature-based time-series analysis. In: G. Dong, H. Liu (eds.) Feature Engineering for Machine Learning and Data Analytics, chap. 4, pp. 87-116. CRC Press (2018) [OpenAIRE]

13. Fulcher, B.D., Jones, N.S.: Highly comparative feature-based time-series classification. IEEE Transactions on Knowledge and Data Engineering 26(12), 3026-3037 (2014). DOI 10.1109/TKDE.2014.2316504

14. Fulcher, B.D., Jones, N.S.: hctsa: A computational framework for automated time-series phenotyping using massive feature extraction. Cell Systems 5(5), 527-531 (2017). DOI 10.1016/j.cels.2017.10.001

15. Fulcher, B.D., Little, M.A., Jones, N.S.: Highly comparative time-series analysis: the empirical structure of time series and their methods. Journal of the Royal Society Interface 10(83), 20130048 (2013). DOI 10.1098/rsif.2013.0048

33 references, page 1 of 3
Abstract
Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through systematic comparison across a comprehensive time-series feature library, such as those in the hctsa toolbox. However, this approach is computationally expensive and involves evaluating many similar features, limiting the widespread adoption of feature-based representations of time series for real-world applications. In this work, we introduce a met...
Subjects
free text keywords: Computer Networks and Communications, Information Systems, Computer Science Applications, Computer Science - Information Retrieval, Computer Science - Machine Learning, Statistics - Machine Learning, Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Information Systems, Time series, Classification, Clustering, Time-series features, Artificial Intelligence & Image Processing, 0801 Artificial Intelligence and Image Processing, 0804 Data Format, 0806 Information Systems, Scaling, Dimensionality reduction, Autocorrelation, Computation, Linear scale, Artificial intelligence, business.industry, business, Pattern recognition, Outlier, Feature vector, Cluster analysis, Computer science
Related Organizations
33 references, page 1 of 3

1. Bagnall, A., Davis, L.M., Hills, J., Lines, J.: Transformation based ensembles for time series classification. In: Proceedings of the 2012 SIAM International conference on data mining, pp. 307-318 (2012)

2. Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: 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 (2017). DOI 10.1007/s10618-016-0483-9

3. Bagnall, A., Lines, J., Hills, J., Bostrom, A.: Time-series classification with COTE: The collective of transformation-based ensembles. 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016 27(9), 1548-1549 (2016). DOI 10.1109/ICDE.2016. 7498418

4. Bagnall, A., Lines, J., Vickers, W., Keogh, E.: The UEA & UCR Time Series Classification Repository. URL http://www.timeseriesclassification.com/

5. Bandara, K., Bergmeir, C., Smyl, S.: Forecasting across time series databases using long short-term memory networks on groups of similar series: a clustering approach. arXiv (2017). DOI 10.1002/pdi.718. URL http://arxiv.org/abs/1710.03222

6. Berndt, D., Clifford, J.: Using dynamic time warping to find patterns in time series. In: Workshop on Knowledge Knowledge Discovery in Databases, vol. 398, pp. 359-370 (1994)

7. Biason, A., Pielli, C., Rossi, M., Zanella, A., Zordan, D., Kelly, M., Zorzi, M.: ECCENTRIC: An energy- and context-centric perspective on IoT systems and protocol design. IEEE Access 5, 6894-6908 (2017). DOI 10.1109/ACCESS.2017.2692522

8. Dau, H.A., Bagnall, A., Kamgar, K., Yeh, C.M., Zhu, Y.: UCR time series archive 2018. arXiv (2018)

9. Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in timeseries databases. In: SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on management of data, pp. 419-429 (1994)

10. Fisher, R.A.: Statistical Methods for Research Workers (1925). DOI 52,281-302

11. Fulcher, B.D.: 1000 empirical time series (2017). DOI https://doi.org/10.4225/03/ 59c88e1e51868

12. Fulcher, B.D.: Feature-based time-series analysis. In: G. Dong, H. Liu (eds.) Feature Engineering for Machine Learning and Data Analytics, chap. 4, pp. 87-116. CRC Press (2018) [OpenAIRE]

13. Fulcher, B.D., Jones, N.S.: Highly comparative feature-based time-series classification. IEEE Transactions on Knowledge and Data Engineering 26(12), 3026-3037 (2014). DOI 10.1109/TKDE.2014.2316504

14. Fulcher, B.D., Jones, N.S.: hctsa: A computational framework for automated time-series phenotyping using massive feature extraction. Cell Systems 5(5), 527-531 (2017). DOI 10.1016/j.cels.2017.10.001

15. Fulcher, B.D., Little, M.A., Jones, N.S.: Highly comparative time-series analysis: the empirical structure of time series and their methods. Journal of the Royal Society Interface 10(83), 20130048 (2013). DOI 10.1098/rsif.2013.0048

33 references, page 1 of 3
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