Retail Demand Forecasting for Small-Medium Enterprises During COVID-19 Pandemic: Case Studies Based on Romanian Convenience Stores

Author:

Savan Emanuel-Emil,Gica Oana,Sofica Aurelian

Publisher

Springer International Publishing

Reference40 articles.

1. Anderson, D. R., Sweeney, D. J., Williams, T. A., Freeman J., & Shoesmith, E. (2007) Statistics for Business and Economics, International Edition. Thomson, London.

2. Arora (2020) A global view of how consumer behaviour is changing amid COVID-19 [WWW Document]. URL https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/a-global-view-of-how-consumer-behavior-is-changing-amid-covid-19#. Accessed on 22 Aug 2020.

3. Arunraj, N. S., & Ahrens, D. (2015). A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting. International Journal of Production Economics, 170, 321–335. https://doi.org/10.1016/j.ijpe.2015.09.039.

4. Census (2011). Romanian 2011 Census [WWW Document]. URL http://www.recensamantromania.ro/rezultate-2/. Accessed on 16 July 2020.

5. Chen, F. L., & Ou, T. Y. (2011). Sales forecasting system based on gray extreme learning machine with Taguchi method in retail industry. Expert Systems with Applications, 38(3), 1336–1345. https://doi.org/10.1016/j.eswa.2010.07.014.

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