Evangelos Spiliotis
Evangelos Spiliotis
Forecasting & Strategy Unit, School of Electrical and Computer Engineering, National Technical
Verified email at fsu.gr - Homepage
Cited by
Cited by
Statistical and Machine Learning forecasting methods: Concerns and ways forward
S Makridakis, E Spiliotis, V Assimakopoulos
PloS one 13 (3), e0194889, 2018
The M4 Competition: Results, findings, conclusion and way forward
S Makridakis, E Spiliotis, V Assimakopoulos
International Journal of Forecasting 34 (4), 802-808, 2018
The M4 Competition: 100,000 time series and 61 forecasting methods
S Makridakis, E Spiliotis, V Assimakopoulos
International Journal of Forecasting 36 (1), 54-74, 2020
Are forecasting competitions data representative of the reality?
E Spiliotis, A Kouloumos, V Assimakopoulos, S Makridakis
International Journal of Forecasting 36 (1), 37-53, 2020
Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption
E Spiliotis, F Petropoulos, N Kourentzes, V Assimakopoulos
Applied Energy 261, 114339, 2020
The M5 accuracy competition: Results, findings and conclusions
S Makridakis, E Spiliotis, V Assimakopoulos
Int J Forecast, 2020
Objectivity, reproducibility and replicability in forecasting research
S Makridakis, V Assimakopoulos, E Spiliotis
International Journal of Forecasting 34 (4), 835-838, 2018
How “OPTIMUS” is a city in terms of energy optimization? e-SCEAF: A web based decision support tool for local authorities
I Papastamatiou, H Doukas, E Spiliotis, J Psarras
Information Fusion 29, 149-161, 2016
Decision support for intelligent energy management in buildings using the thermal comfort model
V Marinakis, H Doukas, E Spiliotis, I Papastamatiou
International Journal of Computational Intelligence Systems 10 (1), 882-893, 2017
Proposing a Smart City Energy Assessment Framework linking local vision with data sets
S Androulaki, E Spiliotis, H Doukas, I Papastamatiou, J Psarras
IISA 2014, The 5th International Conference on Information, Intelligence …, 2014
Forecasting with a hybrid method utilizing data smoothing, a variation of the Theta method and shrinkage of seasonal factors
E Spiliotis, V Assimakopoulos, K Nikolopoulos
International Journal of Production Economics 209, 92-102, 2019
Improving the forecasting performance of temporal hierarchies
E Spiliotis, F Petropoulos, V Assimakopoulos
Plos one 14 (10), e0223422, 2019
The accuracy of machine learning (ML) forecasting methods versus statistical ones: extending the results of the M3-Competition
S Makridakis, E Spiliotis, V Assimakopoulos
Working Paper, University of Nicosia, 2017
Predicting/hypothesizing the findings of the M4 Competition
S Makridakis, E Spiliotis, V Assimakopoulos
International Journal of Forecasting 36 (1), 29-36, 2020
Generalizing the Theta method for automatic forecasting
E Spiliotis, V Assimakopoulos, S Makridakis
European Journal of Operational Research 284 (2), 550-558, 2020
Responses to discussions and commentaries
S Makridakis, E Spiliotis, V Assimakopoulos
International Journal of Forecasting 36 (1), 217-223, 2020
OPTIMUS decision support tools: Transforming multidisciplinary data to energy management action plans
H Doukas, V Marinakis, E Spiliotis, J Psarras
2016 7th International Conference on Information, Intelligence, Systems …, 2016
Forecasting: theory and practice
F Petropoulos, D Apiletti, V Assimakopoulos, MZ Babai, DK Barrow, ...
arXiv preprint arXiv:2012.03854, 2020
Comparison of statistical and machine learning methods for daily SKU demand forecasting
E Spiliotis, S Makridakis, AA Semenoglou, V Assimakopoulos
Operational Research, 1-25, 2020
Hierarchical forecast reconciliation with machine learning
E Spiliotis, M Abolghasemi, RJ Hyndman, F Petropoulos, ...
arXiv preprint arXiv:2006.02043, 2020
The system can't perform the operation now. Try again later.
Articles 1–20