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.
This thesis and the corresponding work were done at the University of Bonn.
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Sagrebin, M., Goerke, N. (2006). Learning Time-Series Similarity with a Neural Network by Combining Similarity Measures. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_13
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DOI: https://doi.org/10.1007/11840930_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38871-5
Online ISBN: 978-3-540-38873-9
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