Learning time-series similarity with a neural network by combining similarity measures

  • Authors:

  • Maria Sagrebin;Nils Goerke

  • Affiliations:

  • Fakultät für Ingenieurwissenschaften, Universität Duisburg-Essen, Germany;Div. of Neural Computation, Dept. of Computer Science, University of Bonn, Germany

  • Venue:
  • ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
  • Year:
  • 2006

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Abstract

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.