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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(4): 283-287 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.521

Time Series Shapelets: Training Time Improvement Based on Particle Swarm Optimization

Ivan S. Mitzev and Nickolas H. Younan
Abstract—Time series classification (TSC) has become a popular research topic in recent years. The TSC works with any real value sequences such as regular discrete time signals as well with time series obtained by conversion from shapes in an image. Recently, time series shapelets classification methods have been used for data mining and time series classification. These methods are proven to have high accuracy and outperform state-of-the-art methods such as the nearest neighbor in cases where the signals are noisy. Furthermore these methods work well with local regions of the signal instead with the whole signal, a technique that in some cases gives better results as the global features are more susceptible to distortions. However, one of the main drawbacks of the time series shapelets classification methods is its slow training time. In this paper, we propose a method that is based on particle swarm optimization (PSO) to improve not only the training time but also performance accuracy when applied to benchmark datasets.

Index Terms—Time series shapelets, particle swarm optimization (PSO), classification.

The authors are with the Mississippi State University, Mississippi State, MS 39762, USA (e-mail: ism6@msstate.edu, younan@ece.msstate.edu).

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Cite: Ivan S. Mitzev and Nickolas H. Younan, "Time Series Shapelets: Training Time Improvement Based on Particle Swarm Optimization," International Journal of Machine Learning and Computing vol. 5, no. 4, pp. 283-287, 2015.

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