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Sales prediction with parametrized time series analysis

  • When forecasting sales figures, not only the sales history but also the future price of a product will influence the sales quantity. At first sight, multivariate time series seem to be the appropriate model for this task. Nontheless, in real life history is not always repeatable, i.e. in the case of sales history there is only one price for a product at a given time. This complicates the design of a multivariate time series. However, for some seasonal or perishable products the price is rather a function of the expiration date than of the sales history. This additional information can help to design a more accurate and causal time series model. The proposed solution uses an univariate time series model but takes the price of a product as a parameter that influences systematically the prediction. The price influence is computed based on historical sales data using correlation analysis and adjustable price ranges to identify products with comparable history. Compared to other techniques this novel approach is easy to compute and allows to preset the price parameter for predictions and simulations. Tests with data from the Data Mining Cup 2012 demonstrate better results than established sophisticated time series methods.

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Author of HS ReutlingenLaux, Friedrich; Petrov, Ilia; Abele, Christian; Schaidnagel, Michael
Erschienen in:DBKDA 2013, the Fifth International Conference on Advances in Databases, Knowledge, and Data Applications : January 27 - February 1, 2013, Seville, Spain
Editor:Friedrich LauxORCiD, Lena Strömbäck
Document Type:Conference proceeding
Publication year:2013
Creating Corporation:International Academy, Research, and Industry Association (IARIA)
Tag:multivariate time series; parametrized predictor; pricesales correlation; sales prediction
Page Number:8
First Page:166
Last Page:173
DDC classes:658 Allgemeines Management
Open access?:Ja
Licence (German):License Logo  Open Access