A shapelet transform for time series classification

Abstract

The problem of time series classification (TSC), where we consider any real-valued ordered data a time series, presents a specific machine learning challenge as the ordering of variables is often crucial in finding the best discriminating features. One of the most promising recent approaches is to find shapelets within a data set. A shapelet is a time series subsequence that is identified as being representative of class membership. The original research in this field embedded the procedure of finding shapelets within a decision tree. We propose disconnecting the process of finding shapelets from the classification algorithm by proposing a shapelet transformation. We describe a means of extracting the k best shapelets from a data set in a single pass, and then use these shapelets to transform data by calculating the distances from a series to each shapelet. We demonstrate that transformation into this new data space can improve classification accuracy, whilst retaining the explanatory power provided by shapelets.

Otolith shape and size: The importance of age when determining indices for fish-stock separation

“…Otolith boundaries are also extracted and represented, or encoded in different ways (transformed) prior to analysis with methods such as Fourier transforms (Begg and Brown 2000;Galley et al 2006;Bani et al 2013); and Elliptical Fourier transforms (Campana and Casselman 1993;Duarte-Neto et al 2008). Other methods of otolith boundary representation include Wavelets (Parisi-Baradad et al 2005), Curvature-Scale-Space (Begg et al 2005;Parisi-Baradad et al 2005) and the more recent Shapelet transform method (Lines et al 2012;Mapp et al 2013;Hills et al 2014).…”

Discovering Malware with Time Series Shapelets

“…Despite the existence of many other approaches for time series classification [38], we use shapelets in our work, as (i) they find local and discriminative features from the data, (ii) they impose no assumptions on the nature of the data unlike autoregressive or ARIMA time series models [38,39] and they work even on non-stationary time series, (iii) they work on data instances of different lengths (unlike popular classifiers such as support vector machines, feed-forward neural networks, and random forests in their standard forms), (iv) they are easy to interpret and visualize for domain experts, and (v) they have been shown to be more accurate than other methods for some datasets [11,12,15,18,[20][21][22][23][24]27,39].…”

“…We consider a binary (two-class) classification scenario. Time series shapelets were first proposed by Ye and Keogh [39] and there have been optimizations on the initial method to make it faster or more advanced [12,15,18,20,24,27].…”

“…This feature is based on the distance of a time series instance to a shapelet discovered from the training data [15,39]. To reiterate, the distance of a time series (typically longer) to a shapelet subsequence (typically shorter) is obtained by sliding the shapelet across the time series and noting the minimum Euclidean distance between the shapelet subsequence and the corresponding time series subsequences.…”

“…Intuitively, shapelet-based approaches focus on finding local discriminative features within the data, and once these are found, the rest of the data is discarded. Shapelets have been used for a wide range of time series data mining tasks [11,15,21,22,23,27].…”