Abstract
When practitioners analyze the similarity between time series, they often use correlation to gauge this similarity. Sometimes this works, but sometimes, this leads to counter-intuitive results, in which case other similarity measures are more appropriate. An important question is how to select an appropriate similarity measures. In this paper, we show, on simple examples, that the use of natural symmetries – scaling and shift – can help with such a selection.
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Acknowledgments
This work is supported by Chiang Mai University, Thailand. It was also supported in part:
– by the National Science Foundation grants HRD-0734825 and HRD-1242122
(Cyber-ShARE Center of Excellence) and DUE-0926721,
– by an award “UTEP and Prudential Actuarial Science Academy and Pipeline Initiative” from Prudential Foundation, and
– by a grant Mexico’s Instituto Politecnico Nacional.
The authors are very thankful to the anonymous referees for valuable suggestions.
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Batyrshin, I., Dumrongpokaphan, T., Kreinovich, V., Kosheleva, O. (2016). How to Select an Appropriate Similarity Measure: Towards a Symmetry-Based Approach. In: Huynh, VN., Inuiguchi, M., Le, B., Le, B., Denoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2016. Lecture Notes in Computer Science(), vol 9978. Springer, Cham. https://doi.org/10.1007/978-3-319-49046-5_39
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DOI: https://doi.org/10.1007/978-3-319-49046-5_39
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