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Time series prediction with automated periodicity detection

  • 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. Nonetheless, 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 based on a calculated periodicity. The price influence is computed based on historical sales data using correlation analysis and adjustable price ranges to identify products with comparable history. The periodicity is calculated based on a novel approach that is based on data folding and Pearson Correlation. Compared to other techniques this 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 as well as artificial data demonstrate better results than established sophisticated time series methods.

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Author of HS ReutlingenLaux, Friedrich
Erschienen in:International journal on advances in systems and measurements
Document Type:Article
Year of Publication:2013
Creating Corporation:International Academy, Research, and Industry Association (IARIA)
Tag:multivariate time series; periodicity mining; sales prediction
Issue:3 & 4
Page Number:11
First Page:394
Last Page:404
DDC classes:004 Informatik
Open Access?:Ja
Licence (German):License Logo  Creative Commons - Namensnennung, nicht kommerziell, Weitergabe unter gleichen Bedingungen