Statistical and Machine Learning forecasting methods: Concerns and ways forward S Makridakis, E Spiliotis, V Assimakopoulos PloS one 13 (3), e0194889, 2018 | 1408 | 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 | 816 | 2020 |
The M4 Competition: Results, findings, conclusion and way forward S Makridakis, E Spiliotis, V Assimakopoulos International Journal of forecasting 34 (4), 802-808, 2018 | 667 | 2018 |
Forecasting: theory and practice F Petropoulos, D Apiletti, V Assimakopoulos, MZ Babai, DK Barrow, ... International Journal of Forecasting 38 (3), 705-871, 2022 | 598 | 2022 |
M5 accuracy competition: Results, findings, and conclusions S Makridakis, E Spiliotis, V Assimakopoulos International Journal of Forecasting 38 (4), 1346-1364, 2022 | 391 | 2022 |
The M5 competition: Background, organization, and implementation S Makridakis, E Spiliotis, V Assimakopoulos International Journal of Forecasting 38 (4), 1325-1336, 2022 | 125 | 2022 |
Comparison of statistical and machine learning methods for daily SKU demand forecasting E Spiliotis, S Makridakis, AA Semenoglou, V Assimakopoulos Operational Research 22 (3), 3037-3061, 2022 | 93 | 2022 |
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 | 87 | 2022 |
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 | 86 | 2022 |
Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption E Spiliotis, F Petropoulos, N Kourentzes, V Assimakopoulos Applied Energy 261, 114339, 2020 | 78 | 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 | 73 | 2020 |
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 | 69 | 2021 |
Hierarchical forecast reconciliation with machine learning E Spiliotis, M Abolghasemi, RJ Hyndman, F Petropoulos, ... Applied Soft Computing 112, 107756, 2021 | 49 | 2021 |
Generalizing the theta method for automatic forecasting E Spiliotis, V Assimakopoulos, S Makridakis European Journal of Operational Research 284 (2), 550-558, 2020 | 42 | 2020 |
ML-based energy management of water pumping systems for the application of peak shaving in small-scale islands E Sarmas, E Spiliotis, V Marinakis, G Tzanes, JK Kaldellis, H Doukas Sustainable Cities and Society 82, 103873, 2022 | 40 | 2022 |
Predicting/hypothesizing the findings of the M4 Competition S Makridakis, E Spiliotis, V Assimakopoulos International Journal of Forecasting 36 (1), 29-36, 2020 | 40 | 2020 |
Statistical, machine learning and deep learning forecasting methods: Comparisons and ways forward S Makridakis, E Spiliotis, V Assimakopoulos, AA Semenoglou, G Mulder, ... Journal of the Operational Research Society 74 (3), 840-859, 2023 | 38 | 2023 |
Objectivity, reproducibility and replicability in forecasting research S Makridakis, V Assimakopoulos, E Spiliotis International Journal of Forecasting 34 (4), 835-838, 2018 | 38 | 2018 |
A meta-learning classification model for supporting decisions on energy efficiency investments E Sarmas, E Spiliotis, V Marinakis, T Koutselis, H Doukas Energy and Buildings 258, 111836, 2022 | 35 | 2022 |
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 | 34 | 2019 |