| Gluonts: Probabilistic and neural time series modeling in python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... Journal of Machine Learning Research 21 (116), 1-6, 2020 | 363* | 2020 |
| 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 | 287* | 2022 |
| Neural temporal point processes: A review O Shchur, AC Türkmen, T Januschowski, S Günnemann International Joint Conference on Artificial Intelligence (IJCAI), 2021 | 92 | 2021 |
| Chronos: Learning the language of time series AF Ansari, L Stella, C Turkmen, X Zhang, P Mercado, H Shen, O Shchur, ... arXiv preprint arXiv:2403.07815, 2024 | 49 | 2024 |
| Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes AC Türkmen, T Januschowski, Y Wang, AT Cemgil PLoS One 16 (11), e0259764, 2021 | 47* | 2021 |
| A review of nonnegative matrix factorization methods for clustering AC Türkmen arXiv preprint arXiv:1507.03194, 2015 | 40 | 2015 |
| Fastpoint: Scalable deep point processes AC Türkmen, Y Wang, AJ Smola Machine Learning and Knowledge Discovery in Databases: European Conference …, 2020 | 29 | 2020 |
| Deep explicit duration switching models for time series AF Ansari, K Benidis, R Kurle, AC Turkmen, H Soh, AJ Smola, B Wang, ... Advances in Neural Information Processing Systems 34, 29949-29961, 2021 | 26 | 2021 |
| AutoGluon–TimeSeries: AutoML for probabilistic time series forecasting O Shchur, AC Turkmen, N Erickson, H Shen, A Shirkov, T Hu, B Wang International Conference on Automated Machine Learning, 9/1-21, 2023 | 22 | 2023 |
| Detecting anomalous event sequences with temporal point processes O Shchur, AC Turkmen, T Januschowski, J Gasthaus, S Günnemann Advances in Neural Information Processing Systems 34, 13419-13431, 2021 | 15 | 2021 |
| Dirichlet–Luce choice model for learning from interactions G Çapan, İ Gündoğdu, AC Türkmen, AT Cemgil User Modeling and User-Adapted Interaction 32 (4), 611-648, 2022 | 7* | 2022 |
| Clustering event streams with low rank Hawkes processes AC Türkmen, G Çapan, AT Cemgil IEEE Signal Processing Letters 27, 1575-1579, 2020 | 6 | 2020 |
| Testing granger non-causality in panels with cross-sectional dependencies L Minorics, C Turkmen, D Kernert, P Bloebaum, L Callot, D Janzing International Conference on Artificial Intelligence and Statistics, 10534-10554, 2022 | 2 | 2022 |
| Text classification with coupled matrix factorization AC Türkmen, AT Cemgil 2016 24th Signal Processing and Communication Application Conference (SIU …, 2016 | 1 | 2016 |
| A flexible forecasting stack T Januschowski, J Gasthaus, YB Wang, S Rangapuram, C Turkmen, ... | | 2024 |
| Fast high-dimensional temporal point processes with applications AC Türkmen Thesis (Ph. D.)-Bogazici University. Institute for Graduate Studies in …, 2020 | | 2020 |
| Testing for Self-excitation in Financial Events: A Bayesian Approach AC Türkmen, AT Cemgil ECML PKDD 2018 Workshops: MIDAS 2018 and PAP 2018, Dublin, Ireland …, 2019 | | 2019 |
| Quantifying Causal Contribution in Rare Event Data AC Turkmen, D Janzing, O Shchur, L Minorics, L Callot A causal view on dynamical systems, NeurIPS 2022 workshop, 0 | | |
