TY - JOUR U1 - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed) A1 - Schaidnagel, Michael A1 - Laux, Fritz T1 - Time series prediction with automated periodicity detection JF - International journal on advances in systems and measurements N2 - 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. KW - sales prediction KW - multivariate time series KW - periodicity mining Y1 - 2013 U6 - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-1135 UN - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-1135 UR - http://www.iariajournals.org/systems_and_measurements/tocv6n34.html SN - 1942-261x SS - 1942-261x VL - 6 IS - 3 & 4 SP - 394 EP - 404 S1 - 11 PB - IARIA ER -