With an efficient time series datasets classification fast accuracy model for dynamic data sets using classical-k-nn algorithm

Abstract

Recent years have seen significant progress in improving both the efficiency and effectiveness of time series classification. However, because of the best solution typically the Fuzzy Nearest Neighbour Algorithm with the relatively expensive Dynamic Time Warping as the distance measure, successful deployments on resource constrained devices remain elusive. Common technique to collect the benefits of Fuzzy Nearest Neighbor Algorithm is without inheriting its time complexity. However, because of the unique property (most) time series data and the centroid typically does not resemble any of the instances, an unintuitive and underappreciated fact. This project shows that it can exploit a recent result to allow meaningful averaging of “warped” times series and this result allows us to create ultra-efficient Nearest “Centroid” classifiers that are at least as accurate as their more lethargic Nearest Neighborcousins.