A scalable shapelet discovery for time series classification
Nattakit Vichit, Chotirat Ratanamahatana · 2018
As time series data become more complex and users expect more sophisticated information, numerous algorithms have been proposed to solve these challenges. Among those algorithms to classify time series data,�shapelet� a discriminative subsequence of time series data is considered a practical approach due to its accurate and insightful classification. However, previously proposed�shapelet�algorithms still suffer from exceedingly high computational complexity, as a result, limiting its scalability to larger datasets. Therefore, in this work�propose a novel algorithm that speeds up�shapelet�discovery process. The�algorithm so called Dual�Increment�Shapelets�(DIS) is a combination of two-layered incremental neural network and filtering process based on subsequence characteristics. Empirical experiments on forty datasets evidently demonstrate that the�proposed work could achieve large speedup while maintaining its accuracy. Unlike the previous algorithm that mainly emphasizes speedup of the search algorithm, DIS essentially reduces the number of�shapelet�candidates based on subsequence characteristics. As a result, The�DIS algorithm could achieve more than three orders of magnitude speedup, comparing with the baseline algorithms, while preserving the accuracy of the state-of-the-art algorithm.��