ARTIFICIAL NEURAL NETWORK BASED DEMAND FORECASTING INTEGRATED WITH FEDERAL FUNDS RATE

Anupa ARACHCHIGE

anupa13@hotmail.com
University of Moratuwa, Faculty of Engineering, Department of Transport and Logistics, Moratuwa (Sri Lanka)

Ranil SUGATHADASA


University of Moratuwa, Faculty of Engineering, Department of Transport and Logistics, Moratuwa (Sri Lanka)

Oshadhi HERATH


University of Moratuwa, Faculty of Engineering, Department of Transport and Logistics, Moratuwa (Sri Lanka)

Amila THIBBOTUWAWA


University of Moratuwa, Faculty of Engineering, Department of Transport and Logistics, Moratuwa (Sri Lanka)

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.


Keywords:

demand forecasting, artificial neural network, price, promotion, federal funds rate

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Published
2021-12-30

Cited by

ARACHCHIGE, A., SUGATHADASA, R., HERATH, O., & THIBBOTUWAWA, A. (2021). ARTIFICIAL NEURAL NETWORK BASED DEMAND FORECASTING INTEGRATED WITH FEDERAL FUNDS RATE. Applied Computer Science, 17(4), 34–44. https://doi.org/10.23743/acs-2021-27

Authors

Anupa ARACHCHIGE 
anupa13@hotmail.com
University of Moratuwa, Faculty of Engineering, Department of Transport and Logistics, Moratuwa Sri Lanka

Authors

Ranil SUGATHADASA 

University of Moratuwa, Faculty of Engineering, Department of Transport and Logistics, Moratuwa Sri Lanka

Authors

Oshadhi HERATH 

University of Moratuwa, Faculty of Engineering, Department of Transport and Logistics, Moratuwa Sri Lanka

Authors

Amila THIBBOTUWAWA 

University of Moratuwa, Faculty of Engineering, Department of Transport and Logistics, Moratuwa Sri Lanka

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