Learning Time-Series Similarity with a Neural Network by Combining Similarity Measures

  • Maria Sagrebin
  • Nils Goerke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


Within this paper we present the approach of learning the non-linear combination of time-series similarity values through a neural network. A wide variety of time-series comparison methods, coefficients and criteria can be found in the literature that are all very specific, and hence apply only for a small fraction of applications. Instead of designing a new criteria we propose to combine the existing ones in an intelligent way by using a neural network. The approach aims to the goal of making the neural network to learn to compare the similarity between two time-series as a human would do. Therefore, we have implemented a set of comparison methods, the neural network and an extension to the learning rule to include a human as a teacher. First results are promising and show that the approach is valuable for learning human judged time-series similarity with a neural network.


Neural Network Similarity Measure Hide Neuron Audio Signal Dynamic Time Warping 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Maria Sagrebin
    • 1
  • Nils Goerke
    • 2
  1. 1.Fakultät für IngenieurwissenschaftenUniversität Duisburg-EssenGermany
  2. 2.Div. of Neural Computation, Dept. of Computer ScienceUniversity of BonnGermany

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