| Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods A Ntakaris, M Magris, J Kanniainen, M Gabbouj, A Iosifidis Journal of Forecasting 37 (8), 852-866, 2018 | 166 | 2018 |
| Tensor representation in high-frequency financial data for price change prediction DT Tran, M Magris, J Kanniainen, M Gabbouj, A Iosifidis 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1-7, 2017 | 80 | 2017 |
| Bayesian learning for neural networks: an algorithmic survey M Magris, A Iosifidis Artificial Intelligence Review 56 (10), 11773-11823, 2023 | 79 | 2023 |
| Benchmark dataset for mid-price prediction of limit order book data A Ntakaris, M Magris, J Kanniainen, M Gabbouj, A Iosifidis arXiv preprint arXiv:1705.03233, 2017 | 33 | 2017 |
| Multi-head temporal attention-augmented bilinear network for financial time series prediction M Shabani, DT Tran, M Magris, J Kanniainen, A Iosifidis 2022 30th European Signal Processing Conference (EUSIPCO), 1487-1491, 2022 | 13 | 2022 |
| Bayesian bilinear neural network for predicting the mid‐price dynamics in limit‐order book markets M Magris, M Shabani, A Iosifidis Journal of Forecasting 42 (6), 1407-1428, 2023 | 12 | 2023 |
| Detrended fluctuation analysis (DFA) M Magris MATLAB Central File Exchange. Retrieved Available at: https://www. mathworks …, 2022 | 8 | 2022 |
| Quasi black-box variational inference with natural gradients for Bayesian learning M Magris, M Shabani, A Iosifidis arXiv preprint arXiv:2205.11568, 2022 | 5 | 2022 |
| Predicting the state of synchronization of financial time series using cross recurrence plots M Shabani, M Magris, G Tzagkarakis, J Kanniainen, A Iosifidis Neural Computing and Applications 35 (25), 18519-18531, 2023 | 4 | 2023 |
| Exact manifold Gaussian variational Bayes M Magris, M Shabani, A Iosifidis arXiv preprint arXiv:2210.14598, 2022 | 4 | 2022 |
| On the simulation of the Hawkes process via Lambert-W functions M Magris arXiv preprint arXiv:1907.09162, 2019 | 3 | 2019 |
| Uncertainty Estimation in Deep Bayesian Survival Models CM Lillelund, M Magris, CF Pedersen 2023 IEEE EMBS International Conference on Biomedical and Health Informatics …, 2023 | 2 | 2023 |
| Long-range auto-correlations in limit order book markets: Inter-and cross-event analysis M Magris, J Kim, E Räsänen, J Kanniainen 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1-7, 2017 | 2 | 2017 |
| Efficient Training of Probabilistic Neural Networks for Survival Analysis CM Lillelund, M Magris, CF Pedersen IEEE Journal of Biomedical and Health Informatics, 2024 | 1 | 2024 |
| Bayesian Survival Analysis by Approximate Inference of Neural Networks CM Lillelund, M Magris, CF Pedersen arXiv preprint arXiv:2404.06421, 2024 | 1* | 2024 |
| Variational Inference for GARCH-family Models M Magris, A Iosifidis Proceedings of the Fourth ACM International Conference on AI in Finance, 541-548, 2023 | 1 | 2023 |
| Approximate Bayes factors for unit root testing M Martin, I Alexandros arXiv preprint arXiv:2102.10048, 2021 | 1 | 2021 |
| Implied volatility smile dynamics in the presence of jumps M Magris, P Barholm, J Kanniainen arXiv preprint arXiv:1711.02925, 2017 | 1 | 2017 |
| Manifold Gaussian Variational Bayes on the Precision Matrix M Magris, M Shabani, A Iosifidis Neural Computation 36 (9), 1744-1798, 2024 | | 2024 |
| Bayesian Survival Analysis by Approximate Inference of Neural Networks C Marius Lillelund, M Magris, C Fischer Pedersen arXiv e-prints, arXiv: 2404.06421, 2024 | | 2024 |
