Affiliations: College of Computer Science and Technology, Zhejiang University, Hangzhou, China
Correspondence:
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Corresponding author. Yabo Dong, College of Computer Science and Technology, Zhejiang University, Hangzhou, China. E-mail: dongyb@zju.edu.cn.
Abstract: Time series classification is a fundamental problem in the time series mining community. Recently, many sophisticated methods which can produce state-of-the-art classification accuracy on the UCR archive have been proposed. Unfortunately, most of them are parameter-laden methods and require fine-tune for different datasets. Besides, training these classifiers is very computationally demanding, which makes them difficult to use in many real-time applications and previously unseen datasets. In this paper, we propose a novel parameter-light algorithm, MDTW, to classify time series. MDTW has a few parameters which do not require any fine-tune and can be chosen arbitrarily because the classification accuracy is largely insensitive to the parameters. MDTW has no training step; thus, it can be directly applied to unseen datasets. MDTW is based on a popular method, namely the nearest neighbor classifier with Dynamic Time Warping (NN-DTW). However, MDTW performs much faster than NN-DTW by representing time series in different resolutions and using filters-and-refine framework to find the nearest neighbor. The experimental results demonstrate that MDTW performs faster than the state-of-the-art, with small losses (<3%) in average classification accuracy. Besides, we embed a technique, prunedDTW, into the MDTW procedure to make MDTW even faster, and show by experiments that this combination can speed up the MDTW from one to five times.
Keywords: Time series classification, Dynamic Time Warping, nearest neighbor, multilevel representations, filters-and-refine
