Discussion of outlier detection methods of purchasing data

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説明

This study essentially evaluates the purchasing histories of supermarkets. Supermarket purchasing-data is often dynamic and can significantly fluctuate via seasonal, bargain-sale, and regular-sale trends. When data are analyzed inclusive of such trends, it is usually necessary to account for these trends within the acquired statistical sets to prevent biasing of results. For this subject evaluation, time-series data were evaluated for each day during a two-year period; however, data trends were ultimately extensively modified during the subject collection period. In addition, detection of outliers (representing a higher day's sales)was successfully carried out at any given time during the data acquisition interval. In this analysis, a two-step process was ultimately employed that converted non-stationary data into stationary data. Firstly, a period of data was contracted; secondly, trend-removal of data was performed through component decomposition (via the use of a state-space model). As the study team applied these two approaches, outliers were regularly detected. Identification of a product sale date, however, could not be adequately represented within the estimated component of a state-space model.

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