publication . Other literature type . Preprint . Article . 2019

Selected through highly comparative time-series analysis

Carl H. Lubba; Sarab S. Sethi; Philip Knaute; Simon R. Schultz; Ben D. Fulcher; Nick S. Jones;
Open Access
  • Published: 09 Aug 2019
  • 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 Science - Information Retrieval, Computer Science - Machine Learning, Statistics - Machine Learning, Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Information Systems, Computer Science, Time series, Classification, Clustering, Time-series features, Artificial Intelligence & Image Processing, 0801 Artificial Intelligence and Image Processing, 0804 Data Format, 0806 Information Systems, Computer Networks and Communications, Information Systems, Computer Science Applications
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) [OpenAIRE]

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) [OpenAIRE]

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) [OpenAIRE]

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 [OpenAIRE]

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 Science - Information Retrieval, Computer Science - Machine Learning, Statistics - Machine Learning, Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Information Systems, Computer Science, Time series, Classification, Clustering, Time-series features, Artificial Intelligence & Image Processing, 0801 Artificial Intelligence and Image Processing, 0804 Data Format, 0806 Information Systems, Computer Networks and Communications, Information Systems, Computer Science Applications
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) [OpenAIRE]

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) [OpenAIRE]

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) [OpenAIRE]

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 [OpenAIRE]

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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