6 code implementations • 12 Mar 2024 • Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Hao Wang, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang
We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models.
no code implementations • 22 Dec 2023 • Syama Sundar Rangapuram, Jan Gasthaus, Lorenzo Stella, Valentin Flunkert, David Salinas, Yuyang Wang, Tim Januschowski
This paper presents non-parametric baseline models for time series forecasting.
2 code implementations • 11 Jan 2023 • Puya Latafat, Andreas Themelis, Lorenzo Stella, Panagiotis Patrinos
Backtracking linesearch is the de facto approach for minimizing continuously differentiable functions with locally Lipschitz gradient.
no code implementations • NeurIPS 2020 • Emmanuel de Bézenac, Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, Richard Kurle, Lorenzo Stella, Hilaf Hasson, Patrick Gallinari, Tim Januschowski
This paper tackles the modelling of large, complex and multivariate time series panels in a probabilistic setting.
no code implementations • 30 Jul 2020 • Fadhel Ayed, Lorenzo Stella, Tim Januschowski, Jan Gasthaus
Our method is amenable to streaming anomaly detection and scales to monitoring for anomalies on millions of time series.
1 code implementation • 20 May 2020 • Andreas Themelis, Lorenzo Stella, Panagiotis Patrinos
Although the performance of popular optimization algorithms such as Douglas-Rachford splitting (DRS) and the ADMM is satisfactory in small and well-scaled problems, ill conditioning and problem size pose a severe obstacle to their reliable employment.
Optimization and Control 90C06, 90C25, 90C26, 49J52, 49J53
2 code implementations • 21 Apr 2020 • Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Yuyang Wang, Danielle Maddix, Caner Turkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, Francois-Xavier Aubet, Laurent Callot, Tim Januschowski
Deep learning based forecasting methods have become the methods of choice in many applications of time series prediction or forecasting often outperforming other approaches.
7 code implementations • 12 Jun 2019 • Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.
2 code implementations • NeurIPS 2018 • Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski
We present a novel approach to probabilistic time series forecasting that combines state space models with deep learning.
5 code implementations • 20 Jun 2016 • Andreas Themelis, Lorenzo Stella, Panagiotis Patrinos
Extending previous results we show that, despite being nonsmooth for fully nonconvex problems, the FBE still enjoys favorable first- and second-order properties which are key for the convergence results of ZeroFPR.
Optimization and Control 90C06, 90C25, 90C26, 90C53, 49J52, 49J53
2 code implementations • 27 Apr 2016 • Lorenzo Stella, Andreas Themelis, Panagiotis Patrinos
We propose an algorithmic scheme that enjoys the same global convergence properties of FBS when the problem is convex, or when the objective function possesses the Kurdyka-{\L}ojasiewicz property at its critical points.
Optimization and Control