Παρακολούθηση
Evangelos Spiliotis
Evangelos Spiliotis
Forecasting & Strategy Unit, School of Electrical and Computer Engineering, National Technical
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα fsu.gr - Αρχική σελίδα
Τίτλος
Παρατίθεται από
Παρατίθεται από
Έτος
Statistical and Machine Learning forecasting methods: Concerns and ways forward
S Makridakis, E Spiliotis, V Assimakopoulos
PloS one 13 (3), e0194889, 2018
10412018
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
5592020
The M4 Competition: Results, findings, conclusion and way forward
S Makridakis, E Spiliotis, V Assimakopoulos
International Journal of Forecasting 34 (4), 802-808, 2018
5142018
Forecasting: theory and practice
F Petropoulos, D Apiletti, V Assimakopoulos, MZ Babai, DK Barrow, ...
International Journal of Forecasting, 2022
1982022
M5 accuracy competition: Results, findings, and conclusions
S Makridakis, E Spiliotis, V Assimakopoulos
International Journal of Forecasting 38 (4), 1346-1364, 2022
1832022
Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption
E Spiliotis, F Petropoulos, N Kourentzes, V Assimakopoulos
Applied Energy 261, 114339, 2020
602020
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
562020
The M5 competition: Background, organization, and implementation
S Makridakis, E Spiliotis, V Assimakopoulos
International Journal of Forecasting 38 (4), 1325-1336, 2022
522022
The M5 uncertainty competition: Results, findings and conclusions
S Makridakis, E Spiliotis, V Assimakopoulos, Z Chen, A Gaba, I Tsetlin, ...
International Journal of Forecasting 38 (4), 1365-1385, 2022
472022
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
472020
Investigating the accuracy of cross-learning time series forecasting methods
AA Semenoglou, E Spiliotis, S Makridakis, V Assimakopoulos
International Journal of Forecasting 37 (3), 1072-1084, 2021
382021
On the selection of forecasting accuracy measures
D Koutsandreas, E Spiliotis, F Petropoulos, V Assimakopoulos
Journal of the Operational Research Society 73 (5), 937-954, 2022
322022
Hierarchical forecast reconciliation with machine learning
E Spiliotis, M Abolghasemi, RJ Hyndman, F Petropoulos, ...
Applied Soft Computing 112, 107756, 2021
302021
Objectivity, reproducibility and replicability in forecasting research
S Makridakis, V Assimakopoulos, E Spiliotis
International Journal of Forecasting 34 (4), 835-838, 2018
302018
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
292019
Generalizing the theta method for automatic forecasting
E Spiliotis, V Assimakopoulos, S Makridakis
European Journal of Operational Research 284 (2), 550-558, 2020
282020
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, 2017
282017
Improving the forecasting performance of temporal hierarchies
E Spiliotis, F Petropoulos, V Assimakopoulos
PLoS One 14 (10), e0223422, 2019
272019
Predicting/hypothesizing the findings of the M4 Competition
S Makridakis, E Spiliotis, V Assimakopoulos
International Journal of Forecasting 36 (1), 29-36, 2020
262020
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
202016
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