A just-in-time shapelet selection service for online time series classification

Time series classification attracted significant interest over the past decade as a result of the enormous data which can be inserted into the Cyber-Physical System. However, in such system time variant mechanisms violate the stationarity hypothesis which is mostly assumed in the design of classification systems, hence this impairs the accuracy of the classifier. In order to cope with this issue, classifiers with justin-time adaptive training mechanisms are needed, as they allow detecting a change in stationarity and modifying the classifier configuration accordingly to track the process evolution. This paper proposes an online time series classification system including a just-in-time shapelet selection service (JSSS) which selects shapelets as the features for time series classification. The JSSS is based on a fast shapelet selection algorithm (FSS). First, the FSS samples some time series from training dataset with the help of the subclass splitting method. Next, the FSS identifies Local Farthest Deviation Points (LFDPs) from sampled time series; then, the subsequences between two different LFDPs are selected as shapelet candidates. Through these two steps, the number of shapelet candidates is sharply reduced so that the training time is also sharply reduced, which ensures efficient training and feature extraction in an online time series classification system. The experiments showed the JSSS can get results in less than 30 s in the worst condition for all the datasets. At the same time, classification accuracy rates improved by more than 9.9% in the offline scenario and 7.1% in the online scenario. (C) 2019 Elsevier B.V. All rights reserved.