An Effective Lazy Shapelet Discovery Algorithm for Time Series Classification
Shapelet is a primitive for time series classification. As a discriminative local characteristic, it has been studied widely. However, global shapelet-based models have some obvious drawbacks. First, the progress of shapelet extraction is time consuming. Second, the shapelets discovered are merely good on average for the training instances, while local features of each instance to be classified are neglected. For that, instance selection strategy is used to improve the efficiency of feature discovery, and a lazy model based on the local characteristics of each test instance is proposed. Different from the commonly used nearest neighbor models based on global similarity, our model alleviates the uncertainty of predicted class value using local similarity. Experimental results demonstrate that the proposed model is competitive to the benchmarks and can be effectively used to discover characteristics of each time series.
KeywordsTime series Lazy learning Local similarity Shapelet Instance selection
This work is supported by National Natural Science Foundation of China (No. 61672086, 61702030, 61771058), Beijing Natural Science Foundation (No. 4182052), China Postdoctoral Science Foundation (No. 2018M631328) and the Fundamental Research Funds for the Central Universities (No. 2017YJS036, 2018JBM014).
- 2.Nguyen, T.L., Gsponer, S., Ifrim, G.: Time series classification by sequence learning in all-subsequence space. In: 33th International Conference on Data Engineering, pp. 947–958. IEEE Press, San Diego (2017)Google Scholar
- 3.Senin, P., Malinchik, S.: SAX-VSM: interpretable time series classification using sax and vector space model. In: 13th International Conference on Data Mining, pp. 1175–1180. IEEE Press, Dallas (2013)Google Scholar
- 4.Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 947–956. ACM Press, Paris (2009)Google Scholar
- 8.Rakthanmanon, T., Keogh, E.: Fast shapelets: a scalable algorithm for discovering time series shapelets. In: 13th SIAM International Conference on Data Mining, pp. 668–676. SIAM Press, Austin (2013)Google Scholar
- 12.Hou, L., Kwok, J.T., Zurada, J.M.: Efficient learning of timeseries shapelets. In: 30th AAAI Conference on Artificial Intelligence, pp. 1209–1215. AAAI Press, Phoenix (2016)Google Scholar
- 13.Bagnall, A., Lines, J., Vickers, W., Keogh, E.: The UEA & UCR Time Series Classification Repository. www.timeseriesclassification.com