Authors: Bilel Ben Ali; Youssef Masmoudi; Souhail Dhouib
Addresses: LOGIQ Research Unit, University of Sfax, Tunisia ' Saudi Electronic University, Riyadh, KSA ' Faculty of Administrative and Financial Sciences, Al Baha University, Saudi Arabia
Abstract: Dynamic time warping (DTW) consists at finding the best alignment between two time series. It was introduced into pattern recognition and data mining, including many tasks for time series such as clustering and classification. DTW has a quadratic time complexity. Several methods have been proposed to speed up its computation. In this paper, we propose a new variant of DTW called dynamic warping window (DWW). It gives a good approximation of DTW in a competitive CPU time. The accuracy of DWW was evaluated to prove its efficiency. Then the KNN classification was applied for several distance measures (dynamic time warping, derivative dynamic time warping, fast dynamic time warping and DWW). Results show that DWW gives a good compromise between computational speed and accuracy of KNN classification.
Keywords: dynamic time warping; fast DTW; dynamic warping window; DWW; data mining; kNN classification; k-nearest neighbour; time series; FastDTW; upper bounds; pattern recognition.
International Journal of Data Mining, Modelling and Management, 2016 Vol.8 No.2, pp.107 - 123
Received: 28 Aug 2014
Accepted: 09 Oct 2014
Published online: 21 Jun 2016 *