Forecasting Weakly Correlated Time Series in Tasks of Electronic Commerce

Autor:	Kirichenko, Lyudmyla, Radivilova, Tamara, Zinkevich, Illya
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning 90B18 D.4.6 E.3 C.2
Zdroj:	2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT)
Druh dokumentu:	Working Paper
DOI:	10.1109/STC-CSIT.2017.8098793
Popis:	Forecasting of weakly correlated time series of conversion rate by methods of exponential smoothing, neural network and decision tree on the example of conversion percent series for an electronic store is considered in the paper. The advantages and disadvantages of each method are considered. Comment: 4 pages, 4 figures, 1 table
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1904.10927 Zobrazit plný text záznamu View this record from Arxiv