An Improved Feature Extraction Technique for High Volume Time Series Data

J.S. Anstey, D.K. Peters, and C. Dawson (Canada)


Signal processing, segmentation, feature extraction, manu facturing, and time series.


The field of time series data mining has seen an ex plosion of interest in recent years. This interest has flowed over into many applications areas, including fiber manufacturing systems. The volume of time series data generated by a fiber monitoring system can be huge. This limits the applicability of data mining algorithms to this problem domain. A widely used solution is to reduce the data size through feature extraction. Four of the mostly commonly used feature extraction techniques are Fourier transforms, Wavelets, Piecewise Aggregate Approximation, and Piecewise Linear Approximation (PLA). In this paper, we first empirically demonstrate that PLA techniques produce the highest quality features for this problem domain. We then introduce a novel PLA algorithm that is shown to produce higher quality features than any other currently available techniques.

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