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Demand Forecasting Models With Time Series and Random Forest

Demand Forecasting Models With Time Series and Random Forest

Halit Alper Tayali
Copyright: © 2021 |Pages: 24
ISBN13: 9781799858799|ISBN10: 1799858790|ISBN13 Softcover: 9781799868569|EISBN13: 9781799858805
DOI: 10.4018/978-1-7998-5879-9.ch004
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MLA

Tayali, Halit Alper. "Demand Forecasting Models With Time Series and Random Forest." Driving Innovation and Productivity Through Sustainable Automation, edited by Ardavan Amini, et al., IGI Global, 2021, pp. 76-99. https://doi.org/10.4018/978-1-7998-5879-9.ch004

APA

Tayali, H. A. (2021). Demand Forecasting Models With Time Series and Random Forest. In A. Amini, S. Bushell, & A. Mahmood (Eds.), Driving Innovation and Productivity Through Sustainable Automation (pp. 76-99). IGI Global. https://doi.org/10.4018/978-1-7998-5879-9.ch004

Chicago

Tayali, Halit Alper. "Demand Forecasting Models With Time Series and Random Forest." In Driving Innovation and Productivity Through Sustainable Automation, edited by Ardavan Amini, Stephen Bushell, and Arshad Mahmood, 76-99. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-5879-9.ch004

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Abstract

This chapter presents the recent methodological developments in demand management and demand forecasting subjects of the operations management. The background section provides detailed information on the domain of production management, operational analytics, and demand forecasting while providing introductory information on time series forecasting and related machine learning methodologies. The novel contribution of the chapter is the exploration developed in the solutions and recommendations section while examining the effect of stationarity in the time series forecasting methodologies of machine learning with improved benchmark results.

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