Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data

Author:

Chen Zhichao1ORCID,Ding Leilei1ORCID,Chu Zhixuan1ORCID,Qi Yucheng1ORCID,Huang Jianmin1ORCID,Wang Hao1ORCID

Affiliation:

1. Ant Group, Hangzhou, China

Publisher

ACM

Reference21 articles.

1. GluonTS: Probabilistic and Neural Time Series Modeling in Python;Alexandrov Alexander;Journal of Machine Learning Research,2020

2. Shaojie Bai , J Zico Kolter , and Vladlen Koltun . 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 ( 2018 ). Shaojie Bai, J Zico Kolter, and Vladlen Koltun. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018).

3. John Charles Butcher . 2016. Numerical methods for ordinary differential equations . John Wiley & Sons . John Charles Butcher. 2016. Numerical methods for ordinary differential equations. John Wiley & Sons.

4. Zhengping Che , Sanjay Purushotham , Kyunghyun Cho , David Sontag , and Yan Liu . 2018. Recurrent neural networks for multivariate time series with missing values. Scientific reports , Vol. 8 , 1 ( 2018 ), 1--12. Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu. 2018. Recurrent neural networks for multivariate time series with missing values. Scientific reports, Vol. 8, 1 (2018), 1--12.

5. Ricky T. Q. Chen , Yulia Rubanova , Jesse Bettencourt , and David K Duvenaud . 2018 . Neural Ordinary Differential Equations. In Advances in Neural Information Processing Systems, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R . Garnett (Eds.) , Vol. 31 . Curran Associates, Inc., 1--13. https://proceedings.neurips.cc/paper/ 2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud. 2018. Neural Ordinary Differential Equations. In Advances in Neural Information Processing Systems, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.), Vol. 31. Curran Associates, Inc., 1--13. https://proceedings.neurips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf

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