Skip to main content

Research Repository

Advanced Search

Intermittent demand, inventory obsolescence, and temporal aggregation forecasts (2023)
Journal Article
Sanguri, K., Patra, S., Nikolopoulos, K., & Punia, S. (2023). Intermittent demand, inventory obsolescence, and temporal aggregation forecasts. International Journal of Production Research, https://doi.org/10.1080/00207543.2023.2199435

Forecasting for intermittent demand is considered a difficult task and becomes even more challenging in the presence of obsolescence. Traditionally the problem has been dealt with modifications in the conventional parametric methods such as Croston.... Read More about Intermittent demand, inventory obsolescence, and temporal aggregation forecasts.

Social Collateral and consumer payment media during the economic crisis in Europe (2023)
Journal Article
Litsioua, K., & Nikolopoulos, K. (in press). Social Collateral and consumer payment media during the economic crisis in Europe. Journal of Quantitative Finance and Economics,

In this research paper we investigate the relationship between economic crises and the changes in levels of social collateral, as well as the indirect changes in the use of payment media from consumers as a result of the latter. The scene is Europe i... Read More about Social Collateral and consumer payment media during the economic crisis in Europe.

Insights into accuracy of social scientists' forecasts of societal change (2023)
Journal Article
Grossmann, I., Rotella, A., Hutcherson, C. A., Sharpinskyi, K., Varnum, M. E., Achter, S., …Collaborative, T. F. (2023). Insights into accuracy of social scientists' forecasts of societal change. Nature Human Behaviour, 7(4), 484-501. https://doi.org/10.1038/s41562-022-01517-1

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing accuracy of predictions of societal change in domains commonly studied in the... Read More about Insights into accuracy of social scientists' forecasts of societal change.

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., 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.

Radioactivity Monitoring in Ocean Ecosystems (RAMONES). (2021)
Conference Proceeding
Mertzimekis, T., Nomikou, P., Petra, E., Batista, P., Cabesinhas, D., Pascoal, A., …Maigne., L. (2021). Radioactivity Monitoring in Ocean Ecosystems (RAMONES). In GoodIT '21: Proceedings of the Conference on Information Technology for Social Good. https://doi.org/10.1145/3462203.3475906

Natural radioactivity in the marine environment has been present since the Earth's formation, while artificial radionuclides were introduced into the oceans in 1944. More recent direct sources exist that feed the oceans, such as low-level liquid disc... Read More about Radioactivity Monitoring in Ocean Ecosystems (RAMONES)..

Operational Research in the time of COVID-19: the ‘science for better’ or worse in the absence of hard data (2021)
Journal Article
Nikolopoulos, K., Tsinopoulos, C., & Vasilakis, C. (2023). Operational Research in the time of COVID-19: the ‘science for better’ or worse in the absence of hard data. Journal of the Operational Research Society, 74(2), 448-449. https://doi.org/10.1080/01605682.2021.1930208

How can policymakers and planners make informed decisions during a pandemic? What kind of (big) data are needed, and who and when is supposed to provide these? With the Operational Research community unable to get hold of reliable hard data – especia... Read More about Operational Research in the time of COVID-19: the ‘science for better’ or worse in the absence of hard data.

Aggregate selection, individual selection, and cluster selection: an empirical evaluation and implications for systems research (2021)
Journal Article
Vangumalli, D., Nikolopoulos, K., & Litsiou, K. (2021). Aggregate selection, individual selection, and cluster selection: an empirical evaluation and implications for systems research. Cybernetics and Systems, 52(7), 553-578. https://doi.org/10.1080/01969722.2021.1902049

Data analysts when forecasting large number of time series, they regularly employ one of the following methodological approaches: either select a single forecasting method for the entire dataset (aggregate selection), or use the best forecasting meth... Read More about Aggregate selection, individual selection, and cluster selection: an empirical evaluation and implications for systems research.

Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions (2020)
Journal Article
Nikolopoulos, K., Punia, S., Schäfers, A., Tsinopoulos, C., & Vasilakis, C. (2020). Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Operational Research, 290(1), 99-115. https://doi.org/10.1016/j.ejor.2020.08.001

Policymakers during 1COVID-19 operate in uncharted 2territory and must make tough decisions. Operational Research - the ubiquitous ‘science of better’ - plays a vital role in supporting this decision-making process. To that end, using data from the U... Read More about Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions.

Non-Negativity of a Quadratic form with Applications to Panel Data Estimation, Forecasting and Optimization (2020)
Journal Article
Pochiraju, B., Seshadri, S., Thomakos, D. D., & Nikolopoulos, K. (2020). Non-Negativity of a Quadratic form with Applications to Panel Data Estimation, Forecasting and Optimization. Stats, 3(3), 185-202. https://doi.org/10.3390/stats3030015

For a symmetric matrix B, we determine the class of Q such that QtBQ is non-negative definite and apply it to panel data estimation and forecasting: the Hausman test for testing the endogeneity of the random effects in panel data models. We show that... Read More about Non-Negativity of a Quadratic form with Applications to Panel Data Estimation, Forecasting and Optimization.

Superforecasting reality check: Evidence from a small pool of experts and expedited identification (2020)
Journal Article
Katsagounos, I., Thomakos, D. D., Litsiou, K., & Nikolopoulos, K. (2021). Superforecasting reality check: Evidence from a small pool of experts and expedited identification. European Journal of Operational Research, 289(1), 107-117. https://doi.org/10.1016/j.ejor.2020.06.042

Superforecasting has drawn the attention of academics - despite earlier contradictory findings in the literature, arguing that humans can consistently and successfully forecast over long periods. It has also enthused practitioners, due to the major i... Read More about Superforecasting reality check: Evidence from a small pool of experts and expedited identification.

We need to talk about intermittent demand forecasting (2020)
Journal Article
Nikolopoulos, K. (2021). We need to talk about intermittent demand forecasting. European Journal of Operational Research, 291(2), 549-559. https://doi.org/10.1016/j.ejor.2019.12.046

Operational Research (OR) is the ‘science of better’. People constantly try to get better, in practically all aspects of their personal and professional life, and thus OR is de facto a ubiquitous science. What might however not be that clear, is that... Read More about We need to talk about intermittent demand forecasting.