Professor Kostas Nikolopoulos' Outputs (4)

The EU project RAMONES – continuous, long-term autonomous monitoring of underwater radioactivity (2022)
Book Chapter
Nikolopoulos, K. (2022). The EU project RAMONES – continuous, long-term autonomous monitoring of underwater radioactivity. In P. Batista, D. Cabecinhas, L. Sebastião, A. Pascoal, T. Mertzimekis, K. Kebkal, …L. Maigne (Eds.), . Hydrographic Institute

While radioactivity has always existed in the marine environment due to natural phenomena, artificial sources have made their way into the oceans more recently, either through low-level liquid discharges from reprocessing plants, more threatening lar... Read More about The EU project RAMONES – continuous, long-term autonomous monitoring of underwater radioactivity.

Fathoming empirical forecasting competitions’ winners (2022)
Journal Article
Alroomi, A., Karamatzanis, G., Nikolopoulos, K., Tilba, A., & Xiao, S. (2022). Fathoming empirical forecasting competitions’ winners. International Journal of Forecasting, 38(4), 1519-1525. https://doi.org/10.1016/j.ijforecast.2022.03.010

The M5 forecasting competition has provided strong empirical evidence that machine learning methods can outperform statistical methods: in essence, complex methods can be more accurate than simple ones. This result, be as it may, challenges the flags... Read More about Fathoming empirical forecasting competitions’ winners.

RAMONES and Environmental Intelligence: Progress Update (2022)
Book Chapter
Mertzimekis, T., Lagaki, V., Madesis, I., Siltzovalis, G., Petra, E., Nomikou, P., …Maigne, L. (2022). RAMONES and Environmental Intelligence: Progress Update. In GoodIT '22: Proceedings of the 2022 ACM Conference on Information Technology for Social Good (244-249). ACM. https://doi.org/10.1145/3524458.3547255

RAMONES is an EU H2020 FET Proactive Project which aims to offer a new fleet of instruments to perform continuous and in situ measurements of natural and artificial radioactivity in the marine environment as part of its main objectives. Those instrum... Read More about RAMONES and Environmental Intelligence: Progress Update.

Statistical, Machine Learning and Deep Learning forecasting methods: Comparisons and ways forward (2022)
Journal Article
Makridakis, S., Spiliotis, E., Assimakopoulos, V., Semenoglou, A.-A., Mulder, G., & Nikolopoulos, K. (2023). Statistical, Machine Learning and Deep Learning forecasting methods: Comparisons and ways forward. Journal of the Operational Research Society, 74(3), 840-859. https://doi.org/10.1080/01605682.2022.2118629

The purpose of this paper is to test empirically the value currently added by Deep Learning (DL) approaches in time series forecasting by comparing the accuracy of some state-of-the- art DL methods with that of popular Machine Learning (ML) and stati... Read More about Statistical, Machine Learning and Deep Learning forecasting methods: Comparisons and ways forward.