Trend and Value Based Time Series Representation for Similarity Search
Aminata Kane · 2017
Research in time series (TS) knowledge discovery in general, and in similarity search in particular has been very active in recent years, due to the number of application domains that are progressively requiring to work with large amounts of high dimensional TS data. Unfortunately, for many practical applications, the high dimensionality in such frameworks makes it difficult to uncover important patterns from the raw data. Hence TS transformation techniques have become important preprocessing tools for many pattern recognition tasks. In this paper we investigate the problem of similarity search in TS and proposea symbolic transformation process that incorporates the TS value and trend information to enhance accuracy in the search results, and a symbolic similarity measure. We conduct numerous experiments to evaluate the performance of the proposed technique. Our results indicate a better capture of the time series characteristics, while providing increased accuracy and efficiency.