Abstract
In this article, we propose a novel approach to transforming financial time-series values into the symbolic representation based on value changes. Such approach seems to have a few advantages over existing approaches, while one of the most obvious is noise reduction in the data and possibility to find patterns which are universal for investigating different currency pairs. To achieve the goal we introduce the preprocessing method allowing the initial data transformation. We also define a text-based similarity measure which can be used as an alternative for methods allowing to find exact patterns in the historical data.
The proposed approach is experimentally verified on 10 different currency pairs, each covering approximately period of 10 years.
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Kania, K., Juszczuk, P., Kozak, J. (2018). Investigating Patterns in the Financial Data with Enhanced Symbolic Description. In: Nguyen, N., Pimenidis, E., Khan, Z., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2018. Lecture Notes in Computer Science(), vol 11056. Springer, Cham. https://doi.org/10.1007/978-3-319-98446-9_32
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DOI: https://doi.org/10.1007/978-3-319-98446-9_32
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