Microsoft Time Series algorithm application for sales forecasting
Keywords:
forecasting, ARIMA, ARTXP, Microsoft Time Series algorithm
Abstract
The article deals with the forecasting of product sales by known sales history without additional factors with the help of Microsoft Time Series algorithm. The study is concentrated on the analysis of the advantages and drawbacks of the given algorithm. Authors give the conclusions about the expediency of Microsoft Time Series algorithm application in product sales forecasting. The outcomes of practical Microsoft Time Series algorithm leveraging proved its effectiveness in forecasting sales based on historical data.
References
TreePlan Software. Introduction to Decision Trees. Available from: .
Pandey A.K. Early Software Reliability Prediction: A Fuzzy Logic Approach / A.K. Pandey, N.K. Goyal. – New Dehli: Springer India, 2013. – 153 p.
Xu G. Applied Data Mining / Guandong Xu, Yu Zong, Zhenglu Yang.–CRC Press, 2013.–284 p.
Gordon L. Using Classification and Regression Trees (CART) in SAS Enterprise Miner For Applications in Public Health / Leonard Gordon // SAS Global Forum 2013. Paper 089-2013. – 2013. – 8 p.
Loh W.-Y. Classification And Regression Trees / W.-Y. Loh // WIREs Data Mining and Knowledge Discovery.–2011.–Vol.1.–pp.14-23.
Lytvynenko T.I. Problem of data analysis and forecasting using decision trees method / T.I. Lytvynenko // Problems in programming. – 2016. – Vol.2-3. – pp.220-226.
Wu X. Top 10 Algorithms In Data Mining / Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus Ng, Bing Liu, Philip S. Yu, Zhi-Hua Zhou, Michael Steinbach, David J. Hand, Dan Steinberg // Knowledge and Information Systems. – 2008. – Vol.14, Iss.1. – pp.1-37.
Skiena S. The Algorithm Design Manual / Steven S. Skiena. – London: Springer-Verlag, 2008. – 730 p.
Bright Hub Project Management. Disadvantages to Using Decision Trees. Available from:
Pandey A.K. Early Software Reliability Prediction: A Fuzzy Logic Approach / A.K. Pandey, N.K. Goyal. – New Dehli: Springer India, 2013. – 153 p.
Xu G. Applied Data Mining / Guandong Xu, Yu Zong, Zhenglu Yang.–CRC Press, 2013.–284 p.
Gordon L. Using Classification and Regression Trees (CART) in SAS Enterprise Miner For Applications in Public Health / Leonard Gordon // SAS Global Forum 2013. Paper 089-2013. – 2013. – 8 p.
Loh W.-Y. Classification And Regression Trees / W.-Y. Loh // WIREs Data Mining and Knowledge Discovery.–2011.–Vol.1.–pp.14-23.
Lytvynenko T.I. Problem of data analysis and forecasting using decision trees method / T.I. Lytvynenko // Problems in programming. – 2016. – Vol.2-3. – pp.220-226.
Wu X. Top 10 Algorithms In Data Mining / Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus Ng, Bing Liu, Philip S. Yu, Zhi-Hua Zhou, Michael Steinbach, David J. Hand, Dan Steinberg // Knowledge and Information Systems. – 2008. – Vol.14, Iss.1. – pp.1-37.
Skiena S. The Algorithm Design Manual / Steven S. Skiena. – London: Springer-Verlag, 2008. – 730 p.
Bright Hub Project Management. Disadvantages to Using Decision Trees. Available from:
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Published
2020-02-28
How to Cite
StepanenkoА., & KhlevniyА. (2020). Microsoft Time Series algorithm application for sales forecasting. COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, (35), 79-83. Retrieved from http://cit-journal.com.ua/index.php/cit/article/view/76
Section
Automation and Control
