Abstract
In this paper, we present an approach to forecasting the number of paintings that will be sold daily by Vivre Deco S.A. Vivre is an online retailer for Home and Lifestyle in Central and Eastern Europe. One of its concerns is related to the stocks that it needs to make at its own warehouse (considering its limited available space) to ensure a good product flow that would maximize both the company profit and the users’ satisfaction. Since stocks are directly connected to sales, the purpose is to predict the amount of sales from each category of products, given the selling history of these products. Thus, we have chosen a category of products (paintings) and used ARIMA for obtaining the predictions. We present different considerations regarding how we chose the model, along with the solver and the optimization method for fitting ARIMA. We also discuss the influence of the differencing on the obtained results, along with information about the runtime of different models.
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Acknowledgement
This work was partly supported through the GEX contract 20/25.09.2017 funded by the University Politehnica of Bucharest. We would also like to thank Vivre Deco for providing us the data that made this study possible.
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Chiru, CG., Posea, VV. (2018). Time Series Analysis for Sales Prediction. In: Agre, G., van Genabith, J., Declerck, T. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2018. Lecture Notes in Computer Science(), vol 11089. Springer, Cham. https://doi.org/10.1007/978-3-319-99344-7_15
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