ISSN 2353-6977 (Online)
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.
- APA 7th style
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
- Chicago style
Arachchige, Anupa, Ranil Sugathadasa, Oshadhi Herath, and Amila Thibbotuwawa. "Artificial Neural Network Based Demand Forecasting Integrated with Federal Funds Rate." Applied Computer Science 17, no. 4 (2021): 34-44.
- IEEE style
A. Arachchige, R. Sugathadasa, O. Herath, and A. Thibbotuwawa, "Artificial neural network based demand forecasting integrated with Federal Funds Rate," Applied Computer Science, vol. 17, no. 4, pp. 34-44, 2021, doi: 10.23743/acs-2021-27.
- Vancouver style
Arachchige A, Sugathadasa R, Herath O, Thibbotuwawa A. Artificial neural network based demand forecasting integrated with Federal Funds Rate. Applied Computer Science. 2021;17(4):34-44.
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