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 using shapelets, existing work uses Euclidean distance measure. But Euclidean distance has following limitation : 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. And also we have performed pessimistic pruning on decision tree. The Mahalanobis distance improves the accuracy of algorithm and pessimistic pruning method reduces the time complexity of testing and classification of unseen data. The Mahalanobis distance measure differs from Euclidean distance in that it takes into account the correlations of the data set and is scale-invariant. We show that our algorithm is much more accurate and faster than existing algorithms.
Index Terms—Decision trees, information gain, mahalanobis distance measure, time series classification, shapelets, reduced error pruning.
M. Arathi and A. Govardhan are with Jawaharlal Nehru Technological University Hyderabad, Hyderabad-500085, Andhra Pradesh, India (e-mail: arathi.jntu@gmail.com).
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Cite: M. Arathi and A. Govardhan, " An Efficient and Accurate Time Series Classification Using Shapelets," International Journal of Information and Electronics Engineering vol. 4, no. 5, pp. 347-353, 2014.
