A Novel Dynamic Time Wrapping Similarity Algorithm Optimized by Multi-Granularity

다국어 초록 (Multilingual Abstract)

Dynamic time warping algorithm (DTW) is a method of measuring the similarity of
time series. Concerning the problem that DTW cannot keep high classification accuracy
when the computation speed improved, a FG-DTW method based on the idea of naive
granular computing is proposed. In this method, firstly, better temporal granularity is
acquired by calculating temporal variance feature and it is used to replace original time
series; Secondly, the elastic size of under comparing time series granularity allow
dynamic adjustment through DTW algorithm and optimal time series corresponding
granularity is obtained; Finally, DTW distance is calculated by optimal corresponding
granularity model. At the same time, the early termination strategy of infimum function is
introduced to improve the efficiency of FG-DTW algorithm. Experiments show that the
proposed algorithm improves the running rate and accuracy effectively.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 2. Dynamic Time Warping Similarity Algorithm
• 3. Multi-granularity DTW Model
• 3.1. Granulation Partition Based on Time Series Variance
• 3.2. The Optimal Coarse Grain Size Division Model
• 3.3. FG-DTW Synthesis Algorithm
• 4. Experimental Program
• 4.1. Introduction of Experiment
• 4.2. Experiment One
• 4.3. Experiment Two
• 5. Conclusion
• References