Abstract—Time series data are sequences of values measured
over time. One of the most recent approaches to classification of
time series data is to find shapelets within a data set. Time series
shapelets are time series subsequences which represent a class.
In order to compare two time series sequences, existing work
uses Euclidean distance measure. The problem with Euclidean
distance is that it requires data to be standardized if scales differ.
In this paper, we perform classification of time series data using
time series shapelets and used Mahalanobis distance measure.
The Mahalanobis distance is a descriptive statistic that provides
a relative measure of a data point's distance (residual) from a
common point. The Mahalanobis distance is used to identify and
gauge similarity of an unknown sample set to a known one. It
differs from Euclidean distance in that it takes into account the
correlations of the data set and is scale-invariant. We show that
use of Mahalanobis distance measure instead of Euclidean
distance measure in time series dataset classification using
shapelets leads to increase in accuracy.
Index Terms—Decision trees, Mahalanobis distance measure, time series classification, shapelets.
M. Arathi is with Jawaharlal Nehru Technological University Hyderabad, Hyderabad-500085, Andhra Pradesh, India (e-mail: firstname.lastname@example.org).
A. Govardhan is with Jawaharlal Nehru Technological University Hyderabad, Hyderabad-500085, Andhra Pradesh, India (e-mail: email@example.com).
Cite: M. Arathi and A. Govardhan, "Performance of Mahalanobis Distance in Time Series Classification Using Shapelets," International Journal of Machine Learning and Computing vol.4, no. 4, pp. 339-345, 2014.