K
Konstantinos Nikolopoulos
Researcher at Bangor University
Publications - 123
Citations - 4737
Konstantinos Nikolopoulos is an academic researcher from Bangor University. The author has contributed to research in topics: Demand forecasting & Decision support system. The author has an hindex of 27, co-authored 122 publications receiving 3595 citations. Previous affiliations of Konstantinos Nikolopoulos include Lancaster University & Durham University.
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The theta model: A decomposition approach to forecasting
TL;DR: In this article, the authors proposed a new univariate forecasting method based on the concept of modifying the local curvature of the time series through a coefficient "Theta" (the Greek letter θ), that is applied directly to the second differences of the data.
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Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning
TL;DR: In this article, a detailed analysis revealed that, while the relatively larger adjustments tended to lead to greater average improvements in accuracy, the smaller adjustments often damaged accuracy, and positive adjustments which involved adjusting the forecast upwards, were much less likely to improve accuracy than negative adjustments.
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Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions.
Konstantinos Nikolopoulos,Sushil Punia,Andreas Schäfers,Christos Tsinopoulos,Chrysovalantis Vasilakis +4 more
TL;DR: Predictive analytics tools for forecasting and planning during a pandemic using statistical, epidemiological, machine- and deep-learning models, and a new hybrid forecasting method based on nearest neighbors and clustering are provided.
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Forecasting and operational research : a review
TL;DR: It is argued that the unique contribution that OR can continue to make to forecasting is through developing models that link the effectiveness of new forecasting methods to the organizational context in which the models will be applied.
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Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction
TL;DR: The NN3 results highlight the ability of NN to handle complex data, including short and seasonal time series, beyond prior expectations, and thus identify multiple avenues for future research.