Supplementary material to the paper

 

Robust Monitoring of Time Series
with Application to Fraud Detection

 

Peter Rousseeuw Domenico Perrotta Marco Riani Mia Hubert
Department of Mathematics Joint Research Centre, Department of Economics and Management Department of Mathematics  
University of Leuven, European Commission University of Parma University of Leuven,  
Belgium Italy Italy Belgium  
peter@rousseeuw.net domenico.perrotta@ec.europa.eu mriani@unipr.it mia.hubert@kuleuven.be  

Abstract


Time series often contain outliers and level shifts or structural changes. These unexpected events are of the utmost importance in fraud detection, as they may pinpoint suspicious transactions. The presence of such unusual events can easily mislead conventional time series analysis and yield erroneous conclusions. In this paper we provide a unified framework for detecting outliers and level shifts in short time series that may have a seasonal pattern. The approach combines ideas from the FastLTS algorithm for robust regression with alternating least squares. The double wedge plot is proposed, a graphical display which indicates outliers and potential level shifts. The methodology is illustrated on real and artificial time series.

 

Data used in the paper

Time series 1, regarding trade of “plants”:  P_12119085.txt

Time series 2, regarding trade of “sugars”:  P_17049075.txt

Simulated data: ysimout.txt

 

Matlab code used in the paper

routine LTSts.m. Link to the documentaion page of LTSts 

routine simulateTS.m. Link to the documentaion page of simulateTS 

routine forecastTS.m. Link to the documentaion page of forecastTS 

routine wedgeplot.m. Link to the documenation page of wedgeplot

Remark: the above routines need the FSDA toolbox

Last modified 16/06/2018 08.01.10