A Shapelet Transform Classification over Uncertain Time Series

Ruizhe Ma, Liangli Zuo, Li Yan

Abstract


A shapelet is a time-series subsequence that can represent local, phase-independent similarity in shape. Time series classification with subsequences can save computing cost, improve computing speed and improve algorithm accuracy. The shapelet-based approaches for time series classification have an advantage of interpretability. Concentrating on uncertain time series, this paper tries to apply the shapelet-based method to classify uncertain time series. Due to the high dimensions of time series, the number of the generated candidate shapelets is generally huge. As a result, the calculation amount is large too. To deal with this problem, in this paper, we introduce a piecewise linear representation (PLR) method for uncertain time series based on key points so that the traditional shapelet discovery algorithm can be improved efficiently. We verify our approach with experiments. The experimental results show that the proposed shapelet algorithm can be used for uncertain time series and it can provide classification accuracy well while reducing time cost.


Keywords


uncertain time series, classification, shapelet, piecewise linear representation

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