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Detect and Correct Abnormal Values in Uncertain Environment: Application to Demand Forecast

Conference paper
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Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 438)

Abstract

This article presents the first results of a study which deals with the detection and the correction of abnormal values in data series intended to forecast demand. This work fits in the broader context of performance management for proximity retailers. Indeed, when this kind of point of sales (POS) is studied, sales volumes are often too small to be effectively exploited by statistical processing methods. It is therefore useful to consolidate the information with expertise and additional knowledge resulting from similar POS. It is also relevant to take into account the inherent uncertainty of such information. The proposal of this paper is a methodological contribution which uses consolidated knowledge to detect and correct abnormal values and to improve the quality of data used to implement forecast methods.

Keywords

Possibility theory Combination rules Similarity measures Forecast 

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Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  1. 1.Laboratoire Génie de Production (LGP), INPT-ENITUniversité de ToulouseTarbesFrance

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