TSX: a novel symbolic representation for financial time series

Authors:
Guiling Li;Liping Zhang;Linquan Yang
Affiliations:
School of Computer Science, China University of Geosciences, Wuhan, China;School of Computer Science, China University of Geosciences, Wuhan, China;Faculty of Information Engineering, China University of Geosciences, Wuhan, China
Venue:
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Year:
2012

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Abstract

Existing symbolic approaches for time series suffer from the flaw of missing important trend feature, especially in financial area. To solve this problem, we present Trend-based Symbolic approximation (TSX), based on Symbolic Aggregate approximation (SAX). First, utilize Piecewise Aggregate Approximation (PAA) approach to reduce dimensionality and discretize the mean value of each segment by SAX. Second, extract trend feature in each segment by recognizing key points. Then, design multiresolution symbolic mapping rules to discretize trend information into symbols. Experimental results show that, compared with traditional symbol approach, our approach not only represents the key feature of time series, but also supports the similarity search effectively and has lower false positives rate.