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Adaptive Piecewise Approximation Based on Weighted Average Approach for Symbolic Time Series Representation

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The process of time series representation has become a significant research problem. It is a challenging task to effectively manage and analyze large volumes of data with high dimensionality. Many high-level representations of time series have been proposed for mining tasks. The selection of effective and scalable method for the appropriate representation of data is another challenge. Piecewise Aggregate Approximation (PAA) method is applied in an effective similarity search of time series to minimize dimensionality by the mean values of equal-sized frames. PAA focus on mean value takes into consideration exclusively the central tendency and not the dispersion present in each segment, which may contribute to some important patterns being missed in some time series data sets and may not yield good tightness of the lower bound. We propose a method based on PAA called Time-weighted Average for Symbolic Aggregate Approximation (TWA_SAX) and compare its performance with the current methods. TWA_SAX enables raw data to be specifically compared to the minimized representation and ensures reduced limits to Euclidean distance. TWA_SAX is utilized to generate faster, more precise algorithms for similarity searches and improves the preciseness of the representation of time series data through enabling better tightness of the lower limit.
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Time Series; Dimensionality Reduction; Piecewise Aggregate Approximation; Similarity Search; Symbolic; Discretize

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