AN IMPROVED FRAMEWORK FOR OUTLIER PERIODIC PATTERN DETECTION IN TIME SERIES USING WALMAT TRANSACTION DATA

Abstract

Periodic pattern detection in time-series is one of the most important data mining task. The periodicity detection of an outlier patterns might be more important than the periodicity of regular, more frequent patterns means periodic pattern .periodic patterns means Patterns which repeat over a period of time. Pattern those which occur unusually or surprisingly called as Outlier Pattern. In this paper ,we present the development of a enhanced spatio-temporal algorithm capable of detecting the periodicity of outlier patterns in a time series using Walmart transaction data and MAD (Median Absolute Deviation) is presented. mean valuesis used in existing algorithm which is not efficient. We have to use MAD which increases the output of these algorithms and gives more accurate information.