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 | 111 | 2022 |
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 | 73 | 2021 |
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 | 70 | 2023 |
Data augmentation for univariate time series forecasting with neural networks AA Semenoglou, E Spiliotis, V Assimakopoulos Pattern Recognition 134, 109132, 2023 | 50 | 2023 |
Image-based time series forecasting: A deep convolutional neural network approach AA Semenoglou, E Spiliotis, V Assimakopoulos Neural Networks 157, 39-53, 2023 | 42 | 2023 |
Neural network ensembles for univariate time series forecasting AA Semenoglou, E Spiliotis, V Assimakopoulos Forecasting with Artificial Intelligence: Theory and Applications, 191-218, 2023 | 1 | 2023 |
Batch Forecasting utilizing ANNs A Semenoglou | | |
From models to pictures AA Semenoglou, E Spiliotis, V Assimakopoulos | | |