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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References
Mondal, P., Shit, L., Goswami, S.: Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices. Int. J. Comput. Sci. Eng. Appl. 4(2), 13 (2014)
Parthiban, K.T., Sekar, I., Umarani, R., Kanna, S.U., Durairasu, P., Rajendran, P.: Industrial Agroforestry Perspectives and Prospectives. Scientific Publishers, Jodhpur (2014)
Whitle, P.: Hypothesis Testing in Time Series Analysis, vol. 4. Almqvist & Wiksells, Uppsala (1951)
Box, G., Jenkins, G.: Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco (1970)
Brockwell, P.J., Davis, R.A.: Time Series: Theory and Methods, p. 273. Springer, New York (1991). https://doi.org/10.1007/978-1-4419-0320-4
Akaike, H.: A new look at the statistical model identification. IEEE Trans. Autom. Control 19(6), 716–723 (1974). https://doi.org/10.1109/tac.1974.1100705
Hyndman, R.J., Athanasopoulos, G.: Forecasting: principles and practice, OTexts. https://www.otexts.org/fpp/8/5. Accessed 20 May 2018
Stergiou, K.I.: Modeling and forecasting the fishery for pilchard (Sardina pilchardus) in Greek waters using ARIMA time-series models. ICES J. Mar. Sci. 46(1), 16–23 (1989)
Meyler, A., Kenny, G., Quinn, T.: Forecasting Irish inflation using ARIMA models. Central Bank and Financial Services Authority of Ireland Technical Paper Series, vol. 1998, no. 3/RT/98, pp. 1–48, December 1998
Contreras, J., Espinola, R., Nogales, F.J., Conejo, A.J.: ARIMA models to predict nextday electricity prices. IEEE Trans. Power Syst. 18(3), 1014–1020 (2003)
Li, Q., Guo, N.N., Han, Z.Y., Zhang, Y.B., Qi, S.X., Xu, Y.G., Wei, Y.M., Han, X., Liu, Y.Y.: Application of an autoregressive integrated moving average model for predicting the incidence of hemorrhagic fever with renal syndrome. Am. J. Trop. Med. Hyg. 87(2), 364–370 (2012)
Kumar, M., Anand, M.: An application of time series ARIMA forecasting model for predicting sugarcane production in India. Stud. Bus. Econ. 9(1), 81–94 (2014)
Sen, P., Roy, M., Pal, P.: Application of ARIMA for forecasting energy consumption and GHG emission: a case study of an Indian pig iron manufacturing organization. Energy 116, 1031–1038 (2016)
Jarrett, J.E., Kyper, E.: ARIMA modeling with intervention to forecast and analyze chinese stock prices. Int. J. Eng. Bus. Manag. 3(3), 53–58 (2011)
Devi, B.U., Sundar, D., Alli, P.: An effective time series analysis for stock trend prediction using ARIMA model for nifty midcap-50. Int. J. Data Min. Knowl. Manag. Process 3(1), 65 (2013)
Taylor, S.J., Letham, B.: Forecasting at scale. PeerJ Preprints 5:e3190v2 (2017). https://doi.org/10.7287/peerj.preprints.3190v2
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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