Cluster Computing

, Volume 22, Supplement 3, pp 7313–7320 | Cite as

A novel time series behavior matching algorithm for online conversion algorithms

  • Iftikhar AhmadEmail author
  • Javeria Iqbal


This work presents a novel time series behavior matching algorithm for analyzing behavior (trend) similarity between two given time series. Unlike traditional approaches, our dynamic programming based approach “Behavior Matching (BM)” is based on trends and behavior rather than absolute distance as similarity measure. In order to compare the effectiveness of our proposed algorithm, we conduct an experimental study on real world stock data (DAX30). We compare our proposed algorithm with state-of-the-art algorithm Euclidean Distance, V-Shift and Dynamic Time Warping. The experimental results validates the performance guarantee and consistency of our proposed scheme.


Time series matching Behavior analysis for online conversion Similarity measures 



We would like to thank the anonymous referees for their valuable input.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Computer Science & Information TechnologyUniversity of Engineering & TechnologyPeshawarPakistan
  2. 2.Punjab University College of Information Technology, University of PunjabLahorePakistan

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