1. Published

   Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions

   Nikolopoulos, K., Punia, S., Schäfers, A., Tsinopoulos, C. & Vasilakis, C., 1 Apr 2021, In: European Journal of Operational Research. 290, 1, p. 99-115 17 p.

   Research output: Contribution to journal › Article › peer-review

2. Published

   The future of credit unions in the United States: evidence from quantitative extrapolations.

   Nikolopoulos, K. & Handrinos, M. C., 1 May 2008, In: Applied Financial Economics Letters. 4, 3, p. 177-182

   Research output: Contribution to journal › Article › peer-review

3. Published

   Relative performance of methods for forecasting special events

   Nikolopoulos, K., Litsa, A., Petropoulos, F., Bougioukos, V. & Khammash, M., 29 Mar 2015, In: Journal of Business Research. 68, 8, p. 1785-1791

   Research output: Contribution to journal › Article › peer-review

4. Published

   Forecasting with quantitative methods: the impact of special events in time series.

   Nikolopoulos, K., 1 Mar 2010, In: Applied Economics. 42, 8, p. 947-955

   Research output: Contribution to journal › Article › peer-review

5. Published

   Forecasting Branded and Generic Pharmaceuticals

   Nikolopoulos, K., Buxton, S., Khammash, M. & Stern, P., 1 Apr 2016, In: International Journal of Forecasting. 32, 2, p. 344-357

   Research output: Contribution to journal › Article › peer-review

6. Published

   Forecasting for big data: does suboptimality matter?

   Nikolopoulos, K. & Petropoulos, F., Oct 2018, In: Computers and Operations Research. 98, p. 322-329

   Research output: Contribution to journal › Article › peer-review

7. Published

   Forecasting Supply Chain sporadic demand with Nearest Neighbor approaches

   Nikolopoulos, K., Babai, M. Z. & Bozos, K., Jul 2016, In: International Journal of Production Economics. 177, July 2016, p. 139-148

   Research output: Contribution to journal › Article › peer-review

8. Published

   An aggregate-disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis.

   Nikolopoulos, K., Syntetos, A. A., Boylan, J. E., Petropoulos, F. & Assimakopoulos, V., 1 Mar 2011, In: Journal of the Operational Research Society. 62, 3, p. 544-554

   Research output: Contribution to journal › Article › peer-review

9. Published

   On the M4.0 forecasting competition: Can you tell a 4.0 earthquake from a 3.0?

   Nikolopoulos, K., Thomakos, D. D., Katsagounos, I. & Alghassab, W., Mar 2020, In: International Journal of Forecasting. 36, 1, p. 203-205

   Research output: Contribution to journal › Article › peer-review

10. Published

    Empirical validation of ELM trained neural networks for financial modelling

    Novykov, V., Bilson, C., Gepp, A., Harris, G. & Vanstone, B., Jan 2023, In: Neural Computing and Applications. 35, 2, p. 1581-1605 25 p.

    Research output: Contribution to journal › Article › peer-review