Ivan S. Mitzev and Nickolas H. Younan, Mississippi State University, United States

Time-series classification is widely used approach for classification. Recent development known as time-series shapelets, based on local patterns from the time-series, shows potential as highly predictive and accurate method for data mining. On the other hand, the slow training time remains an acute problem of this method. In recent years there was a significant improvement of training time performance, reducing the training time in several orders of magnitude. This work tries to maintain low training time- in the range from several second to several minutes for datasets from the popular UCR database, achieving accuracies up to 20% higher than the fastest known up to date method. The goal is achieved by training small 2,3-nodes decision trees and combining their decisions in pattern that uniquely identifies incoming time-series.

Data mining, Time-series shapelets, Combining classifiers