1 code implementation • 15 Mar 2024 • S. Chandra Mouli, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Andrew Stuart, Michael W. Mahoney, Yuyang Wang
Existing work in scientific machine learning (SciML) has shown that data-driven learning of solution operators can provide a fast approximate alternative to classical numerical partial differential equation (PDE) solvers.
2 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, 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 • 25 May 2023 • Hilaf Hasson, Danielle C. Maddix, Yuyang Wang, Gaurav Gupta, Youngsuk Park
Ensembling is among the most popular tools in machine learning (ML) due to its effectiveness in minimizing variance and thus improving generalization.
1 code implementation • 21 Feb 2023 • Derek Hansen, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Michael W. Mahoney
We provide a detailed analysis of ProbConserv on learning with the Generalized Porous Medium Equation (GPME), a widely-applicable parameterized family of PDEs that illustrates the qualitative properties of both easier and harder PDEs.
no code implementations • 4 Feb 2023 • Mike Van Ness, Huibin Shen, Hao Wang, Xiaoyong Jin, Danielle C. Maddix, Karthick Gopalswamy
Meta-forecasting is a newly emerging field which combines meta-learning and time series forecasting.
1 code implementation • 15 Dec 2022 • Xiyuan Zhang, Xiaoyong Jin, Karthick Gopalswamy, Gaurav Gupta, Youngsuk Park, Xingjian Shi, Hao Wang, Danielle C. Maddix, Yuyang Wang
Transformer-based models have gained large popularity and demonstrated promising results in long-term time-series forecasting in recent years.
1 code implementation • 14 Dec 2022 • Nadim Saad, Gaurav Gupta, Shima Alizadeh, Danielle C. Maddix
Numerical experiments based on multiple PDEs with a wide variety of applications indicate that the proposed approach ensures satisfaction of BCs, and leads to more accurate solutions over the entire domain.
no code implementations • NeurIPS Workshop ICBINB 2021 • Ke Alexander Wang, Danielle C. Maddix, Bernie Wang
We consider the problem of probabilistic forecasting over categories with graph structure, where the dynamics at a vertex depends on its local connectivity structure.
1 code implementation • 13 Feb 2021 • Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Hao Wang, Yuyang Wang
Recently, deep neural networks have gained increasing popularity in the field of time series forecasting.
6 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.
no code implementations • 28 May 2019 • Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean Foster, Tim Januschowski
We provide both theoretical and empirical evidence for the soundness of our approach through a necessary and sufficient decomposition of exchangeable time series into a global and a local part.
no code implementations • 30 Nov 2018 • Danielle C. Maddix, Yuyang Wang, Alex Smola
A large collection of time series poses significant challenges for classical and neural forecasting approaches.