Abstract
The characteristic features of time series conversion, which arise in the tasks of e-commerce are described. It is shown that these series are weakly correlated, which does not allow to use traditional methods for their prediction. Forecasting of the series is performed by methods of exponential smoothing, neural network and decision tree using data from an online store. A comparative analysis of the results is carried out. The advantages and disadvantages of each method are considered.
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Kirichenko, L., Radivilova, T., Zinkevich, I. (2018). Comparative Analysis of Conversion Series Forecasting in E-commerce Tasks. In: Shakhovska, N., Stepashko, V. (eds) Advances in Intelligent Systems and Computing II. CSIT 2017. Advances in Intelligent Systems and Computing, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-70581-1_16
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DOI: https://doi.org/10.1007/978-3-319-70581-1_16
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