Outlier detection in distribution companies business using real data set

Outlier detection represents the problem of finding patterns in data that does not fit in expected behaviour. In this paper, outlier detection is done over real transactional data set of the distribution company. Outlier detection is done over time-series data, and over an ordered number of products that can be found within transactions. Unsupervised techniques and methods, S-H-ESD and LOF, are applied because data set is unlabelled. Implementation is performed in R language, and web application dashboard using R Shiny is made. Based on collected results, a proposal for creating the outlier detection and prevention system is made, and ideas for further improvements and additional analysis are given.