A fast and accurate similarity measure for long time series classification based on local extrema and dynamic time warping. (15th April 2021) - Lahreche, Abdelmadjid; Boucheham, Bachir
Highlights: A new parameter-free measure is proposed for classification of long time series. The newly proposed 'LE-DTW' is based on local-extrema and dynamic time warping. Experiments on real-world datasets show efficiency and accuracy of our proposal against popular distance-based methods. Abstract: The problem of similarity measures is a major area of interest within the field of time series classification (TSC). With the ubiquitous of long time series and the increasing demand for analyzing them on limited resource devices, there is a crucial need for efficient and accurate measures to deal with such kind of data. In fact, there are a plethora of good time series similarity measures in the literature. However, most existing methods achieve good performance for short time series, but their effectiveness decreases quickly as time series are longer. In this paper, we develop a new parameter-free measure for the specific purpose of quickly and accurately assessing the similarity between two given long time series. The proposed "Local Extrema Dynamic Time Warping" (LE-DTW) consists of two steps. The first is a time series representation technique that starts by reducing the dimensionality of a given time series using its local extrema. Next, it physically separates the minima and maxima points for more intuitiveness and consistency of the so-obtained time series representation. The second step consists in adapting the Dynamic Time Warping (DTW) measure so as to evaluate the score of similarity between the generated representations. We test the performance of LE-DTW on a wide range of real-world problems from the UCR time series archive for TSC. Experimental results indicate that for short time series, the proposed method achieves reasonable classification accuracy as compared to DTW. However, for long time series, LE-DTW performs much better. Indeed, it outperforms DTW while providing competitive results against popular distance-based classifiers. Moreover, in terms of efficiency, LE-DTW is orders of magnitude faster than DTW.