Arvind Pandiyan
University of Texas, USA
Title: Improving Similarity Search In Irregular Time-Series Using Dynamic Time Warping
Biography
Biography: Arvind Pandiyan
Abstract
Abstract: Dynamic Time Warping (DTW) is one of the prevailing distance measures used in time-series, though it is computationally costly. DTW is providing optimal alignment between two time-series. The time-series show similarity and DTW exploits the existence of similarity. In this paper, we present techniques that can be employed to improve similarity search in irregular time series data. The drawbacks in the classical approach of converting the irregular time series to a regular one before the similarity search techniques are identified and appropriate solutions for overcoming them are implemented. Simulations with real and synthetic data sets reveal that the proposed techniques are performing well with irregular time series data sets.