ARTIFICIAL NEURAL NETWORK BASED DEMAND FORECASTING INTEGRATED WITH FEDERAL FUNDS RATE

Q3 Economics, Econometrics and Finance Applied Computer Science Pub Date : 2021-12-30 DOI:10.35784/acs-2021-27
Anupa Arachchige, Ranil Sugathadasa, Oshadhi Herath, Amila Thibbotuwawa
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引用次数: 5

Abstract

Adverse effects of inaccurate demand forecasts; stockouts, overstocks, customer loss have led academia and the business world towards accurate demand forecasting methods. Artificial Neural Network (ANN) is capable of highly accurate forecasts integrated with many variables. The use of Price and Promotion variables have increased the accuracy while the addition of other relevant variables would decrease the occurrences of errors. The use of the Federal Funds Rate as an additional macroeconomic variable to ANN forecasting models has been discussed in this research by the means of the accuracy measuring method: Average Relative Mean Absolute Error.
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基于人工神经网络的需求预测与联邦基金利率集成
需求预测不准确的不利影响;缺货、库存过剩、客户流失,已经促使学术界和商界采用准确的需求预测方法。人工神经网络(Artificial Neural Network, ANN)具有多变量综合预测精度高的特点。Price和Promotion变量的使用提高了准确性,而添加其他相关变量将减少错误的发生。本研究通过平均相对平均绝对误差的精度测量方法,讨论了在人工神经网络预测模型中使用联邦基金利率作为一个额外的宏观经济变量。
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来源期刊
Applied Computer Science
Applied Computer Science Engineering-Industrial and Manufacturing Engineering
CiteScore
1.50
自引率
0.00%
发文量
0
审稿时长
8 weeks
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