| Deep learning for time series forecasting: Tutorial and literature survey K Benidis, SS Rangapuram, V Flunkert, Y Wang, D Maddix, C Turkmen, ... ACM Computing Surveys 55 (6), 1-36, 2022 | 317* | 2022 |
| Oracle inequalities for high dimensional vector autoregressions AB Kock, L Callot Journal of Econometrics 186 (2), 325-344, 2015 | 261 | 2015 |
| High-dimensional multivariate forecasting with low-rank gaussian copula processes D Salinas, M Bohlke-Schneider, L Callot, R Medico, J Gasthaus Advances in Neural Information Processing Systems 32, 6827-6837, 2019 | 248 | 2019 |
| Criteria for classifying forecasting methods T Januschowski, J Gasthaus, Y Wang, D Salinas, V Flunkert, ... International Journal of Forecasting 36 (1), 167-177, 2020 | 240 | 2020 |
| Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice L Callot, AB Kock, M Medeiros Journal of Applied Econometrics, 2016 | 106* | 2016 |
| A nodewise regression approach to estimating large portfolios L Callot, M Caner, AÖ Önder, E Ulaşan Journal of Business & Economic Statistics 39 (2), 520-531, 2021 | 51 | 2021 |
| Unsupervised model selection for time-series anomaly detection M Goswami, C Challu, L Callot, L Minorics, A Kan The Eleventh International Conference on Learning Representations., 2023 | 40 | 2023 |
| Oracle efficient estimation and forecasting with the adaptive lasso and the adaptive group lasso in vector autoregressions LAF Callot, AB Kock Essays in Nonlinear Time Series Econometrics, 238-268, 2014 | 32 | 2014 |
| Deep generative model with hierarchical latent factors for time series anomaly detection CI Challu, P Jiang, YN Wu, L Callot International Conference on Artificial Intelligence and Statistics, 1643-1654, 2022 | 28 | 2022 |
| Deep learning for forecasting T Januschowski, J Gasthaus, S Rangapuram, L Callot | 26* | 2018 |
| Deterministic and stochastic trends in the Lee–Carter mortality model L Callot, N Haldrup, M Kallestrup-Lamb Applied Economics Letters 23 (7), 486-493, 2016 | 14 | 2016 |
| The problem of natural funnel asymmetries: a simulation analysis of meta‐analysis in macroeconomics L Callot, M Paldam Research Synthesis Methods, 2011 | 14* | 2011 |
| Online false discovery rate control for anomaly detection in time series Q Rebjock, B Kurt, T Januschowski, L Callot Advances in Neural Information Processing Systems 34, 26487-26498, 2021 | 13 | 2021 |
| Sharp Threshold Detection based on Sup-Norm Error Rates in High-dimensional Models L Callot, M Caner, AB Kock, JA Riquelme Journal of Business & Economic Statistics, 2015 | 11 | 2015 |
| Automated evaluation of retrieval-augmented language models with task-specific exam generation G Guinet, B Omidvar-Tehrani, A Deoras, L Callot arXiv preprint arXiv:2405.13622, 2024 | 10 | 2024 |
| Online time series anomaly detection with state space gaussian processes C Bock, FX Aubet, J Gasthaus, A Kan, M Chen, L Callot arXiv preprint arXiv:2201.06763, 2022 | 8 | 2022 |
| Spliced binned-pareto distribution for robust modeling of heavy-tailed time series E Ehrlich, L Callot, FX Aubet arXiv preprint arXiv:2106.10952, 2021 | 7 | 2021 |
| A Simple and Effective Predictive Resource Scaling Heuristic for Large-scale Cloud Applications. Q Rebjock, V Flunkert, T Januschowski, L Callot, J Castellon AIDB@ VLDB, 2020 | 7* | 2020 |
| Vector autoregressions with parsimoniously time varying parameters and an application to monetary policy L Callot, JT Kristensen Tinbergen Institute Discussion Paper 14-145/III, 2015 | 5 | 2015 |
| Improve black-box sequential anomaly detector relevancy with limited user feedback L Kong, L Chen, M Chen, P Bhatia, L Callot arXiv preprint arXiv:2009.07241, 2020 | 4 | 2020 |
Jan GasthausAWS AI Labs, Amazon Web ServicesVerified email at ucl.ac.uk
