Dr Fotios Petropoulos
Senior Lecturer in Management Science
Information, Decision and Operations Division
Key Research Interests:
Decision support systems, Forecasting with judgment, Model selection, Temporal aggregation, Time series forecasting
I am interested in research on time series forecasting, judgmental approaches for forecasting, statistical and judgmental model selection and integrated business forecasting processes. My research so far has focused on the improvement of forecasting processes and more specifically around two streams. First, I have examined how additional information can be extracted from time series data through time transformation (temporal aggregation). Second, I have investigated interactions between forecasting and management judgment.
I welcome enquiries from potential PhD students in the areas of forecasting and behavioural operational research.
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Leading Refereed Journal Papers
Petropoulos, F., Kourentzes, N. & Nikolopoulos, K. Forthcoming. Another look at estimators for intermittent demand. International Journal of Production Economics, DOI:10.1016/j.ijpe.2016.04.017.
Wang, X. & Petropoulos, F. Forthcoming. To select or to combine? The inventory performance of model and expert forecasts. International Journal of Production Research, DOI:10.1080/00207543.2016.1167983.
Petropoulos, F., Goodwin, P. & Fildes, R. Forthcoming. Using a rolling training approach to improve judgmental extrapolations elicited from forecasters with technical knowledge. International Journal of Forecasting, DOI:10.1016/j.ijforecast.2015.12.006.
Kourentzes, N. & Petropoulos, F. Forthcoming. Forecasting with multivariate temporal aggregation: The case of promotional modelling. International Journal of Production Economics, DOI:10.1016/j.ijpe.2015.09.011.
Fioruci, J.A., Pellegrini, T.R., Louzada, F., Petropoulos, F., & Koehler, A.B. 2016. Models for optimising the theta method and their relationship to state space models. International Journal of Forecasting, 32(4): 1151-1161, DOI:10.1016/j.ijforecast.2016.02.005.
Petropoulos, F., Fildes, R. & Goodwin, P. 2016. Do ‘big losses’ in judgmental adjustments to statistical forecasts affect experts’ behaviour? European Journal of Operational Research, 249(3): 842-852, DOI:10.1016/j.ejor.2015.06.002.
Fildes, R. & Petropoulos, F. 2015. Simple versus complex selection rules for forecasting many time series. Journal of Business Research, 68(8): 1692-1701, DOI:10.1016/j.jbusres.2015.03.028.
Nikolopoulos, K., Litsa, A., Petropoulos, F., Bougioukos, V., & Khammash, M. 2015. Relative performance of methods for forecasting Unique Events. Journal of Business Research, 68(8): 1785-1791, DOI:10.1016/j.jbusres.2015.03.037.
Petropoulos, F. & Kourentzes, N. 2015. Forecasts combinations for intermittent demand. Journal of the Operational Research Society, 66(6): 914-924, DOI: 10.1057/jors.2014.62.
Fildes, R. & Petropoulos, F. 2015. Is there a Golden Rule? Journal of Business Research, 68(8): 1742-1745, DOI:10.1016/j.jbusres.2015.01.059.
Spithourakis, G., Petropoulos, F., Nikolopoulos, K., & Assimakopoulos, V. 2015. Amplifying the learning effect via a Forecasting and Foresight Support System. International Journal of Forecasting, 31(1): 20-32, DOI:10.1016/j.ijforecast.2014.05.002.
Petropoulos, F., Makridakis, S., Assimakopoulos, V., & Nikolopoulos, K. 2014. ‘Horses for Courses’ in demand forecasting. European Journal of Operational Research, 237(1): 152-163, DOI:10.1016/j.ejor.2014.02.036.
Kourentzes, N., Petropoulos, F. & Trapero Arenas, J.R. 2014. Improving forecasting by estimating time series structural components across multiple frequencies. International Journal of Forecasting, 30(2): 291-302, DOI:10.1016/j.ijforecast.2013.09.006.
Nikolopoulos, Κ., Syntetos, Α.A., Boylan, J.H., Petropoulos, F., & Assimakopoulos V. 2011. An Aggregate - Disaggregate Intermittent Demand Approach (ADIDA) to forecasting: an empirical proposition and analysis. Journal of the Operational Research Society, 62(3): 544-554, DOI: 10.1057/jors.2010.32
Nikolopoulos, K. & Petropoulos, F. 2016. Misbehaving, misdesigning and miscommunicating. Foresight: The International Journal of Applied Forecasting, 41(Spring 2016): 18-19.
Petropoulos, F. 2015. Forecasting Support Systems: ways forward. Foresight: The International Journal of Applied Forecasting, 39(Fall 2015): 5-11.
Fildes, R. & Petropoulos, F. 2015. Improving forecast quality in practice. Foresight: The International Journal of Applied Forecasting, 36(Winter 2015): 5-12.
Petropoulos, F. & Kourentzes, N. 2014. Improving forecasting via multiple temporal aggregation. Foresight: The International Journal of Applied Forecasting, 34(Summer 2014): 12-17.
Spithourakis, G., Petropoulos, F., Nikolopoulos, K., & Assimakopoulos, V. 2014. A Systemic View of ADIDA framework. IMA Journal of Management Mathematics, 25(2): 125-137, doi: 10.1093/imaman/dps031.
Petropoulos, F., Nikolopoulos, K., Spithourakis, G., & Assimakopoulos, V. 2013. Empirical heuristics for improving Intermittent Demand Forecasting. Industrial Management & Data Systems, 113(5): 683-696, doi: 10.1108/02635571311324142.
Spithourakis, G., Petropoulos, F., Babai, M.Z., Nikolopoulos, K., & Assimakopoulos, V. 2011. Improving the Performance of popular Supply Chain forecasting techniques. Supply Chain Forum, an International Journal, 12(4): 16-25.