Abstract
Concatenated Decision Paths Classification For Time Series Shapelets
Highlights
The time-series shapelets classification method was introduced by Ye and Keogh [1] as a new type of data mining method, that uses the local features of time-series instead of their global
Time performance measurements were produced with a System.Diagnostics.StopWatch .NET class
This paper proposes a new method for time-series shapelets classification, which demonstrates higher accuracies than produced by fastest known state-of-arts method for most of the investigated datasets
Summary
The time-series shapelets classification method was introduced by Ye and Keogh [1] as a new type of data mining method, that uses the local features of time-series instead of their global. That makes it less sensitive to obstructive noise [1] This method is successfully applied to a variety of application areas benefiting from its short classification time and high accuracy. A newly introduced method, named scalable discovery (SD) method [7] shrinks significantly the training time, making the training process to last from portion of a second to several seconds for investigated 45 datasets from UCR collection [9]. It is based on the idea of pruning similar shapelets in the Euclidian space.
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