| Generic and scalable framework for automated time-series anomaly detection N Laptev, S Amizadeh, I Flint Proceedings of the 21th ACM SIGKDD international conference on knowledge …, 2015 | 582 | 2015 |
| Time-series extreme event forecasting with neural networks at uber N Laptev, J Yosinski, LE Li, S Smyl International conference on machine learning 34, 1-5, 2017 | 484 | 2017 |
| Deep and confident prediction for time series at uber L Zhu, N Laptev 2017 IEEE International Conference on Data Mining Workshops (ICDMW), 103-110, 2017 | 463 | 2017 |
| Large-scale unusual time series detection RJ Hyndman, E Wang, N Laptev 2015 IEEE international conference on data mining workshop (ICDMW), 1616-1619, 2015 | 263 | 2015 |
| Early accurate results for advanced analytics on mapreduce N Laptev, K Zeng, C Zaniolo arXiv preprint arXiv:1207.0142, 2012 | 153 | 2012 |
| Neuralprophet: Explainable forecasting at scale O Triebe, H Hewamalage, P Pilyugina, N Laptev, C Bergmeir, ... arXiv preprint arXiv:2111.15397, 2021 | 135 | 2021 |
| Ar-net: A simple auto-regressive neural network for time-series O Triebe, N Laptev, R Rajagopal arXiv preprint arXiv:1911.12436, 2019 | 88 | 2019 |
| Earnings announcement promotions: A Yahoo Finance field experiment A Lawrence, J Ryans, E Sun, N Laptev Journal of Accounting and Economics 66 (2-3), 399-414, 2018 | 84 | 2018 |
| Inertial hidden markov models: Modeling change in multivariate time series G Montanez, S Amizadeh, N Laptev Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 64 | 2015 |
| Very fast estimation for result and accuracy of big data analytics: The EARL system N Laptev, K Zeng, C Zaniolo 2013 IEEE 29th International Conference on Data Engineering (ICDE), 1296-1299, 2013 | 47 | 2013 |
| Fast dimensional analysis for root cause investigation in a large-scale service environment F Lin, K Muzumdar, NP Laptev, MV Curelea, S Lee, S Sankar Proceedings of the ACM on Measurement and Analysis of Computing Systems 4 (2 …, 2020 | 43 | 2020 |
| Reconstruction and regression loss for time-series transfer learning N Laptev, J Yu, R Rajagopal Proceedings of the Special Interest Group on Knowledge Discovery and Data …, 2018 | 42 | 2018 |
| The llama 3 herd of models A Dubey, A Jauhri, A Pandey, A Kadian, A Al-Dahle, A Letman, A Mathur, ... arXiv preprint arXiv:2407.21783, 2024 | 37 | 2024 |
| Smm: A data stream management system for knowledge discovery H Thakkar, N Laptev, H Mousavi, B Mozafari, V Russo, C Zaniolo 2011 IEEE 27th International Conference on Data Engineering, 757-768, 2011 | 35 | 2011 |
| Yahoo Finance search and earnings announcements A Lawrence, J Ryans, E Sun, N Laptev Available at SSRN 2804353, 2016 | 28 | 2016 |
| Anogen: Deep anomaly generator N Laptev Outlier Detection De-constructed (ODD) Workshop, 2018 | 20 | 2018 |
| Yahoo anomaly detection dataset s5 N Laptev, S Amizadeh, Y Billawala URL http://webscope. sandbox. yahoo. com/catalog. php, 2015 | 20 | 2015 |
| FPGA acceleration of mean variance framework for optimal asset allocation A Irturk, B Benson, N Laptev, R Kastner 2008 Workshop on High Performance Computational Finance, 1-8, 2008 | 20 | 2008 |
| Autosteer: Learned query optimization for any sql database C Anneser, N Tatbul, D Cohen, Z Xu, P Pandian, N Laptev, R Marcus Proceedings of the VLDB Endowment 16 (12), 3515-3527, 2023 | 14 | 2023 |
| BOOT-TS: A scalable bootstrap for massive time-series data N Laptev, C Zaniolo, TC Lu, CA Malibu NIPS Proceedings, 1-5, 2012 | 7 | 2012 |
Carlo ZanioloProfessor of Computer Science, University of California, Los Angeles在 cs.ucla.edu 的电子邮件经过验证
