Abstract
Differential equations have proven to be a powerful mathematical tool in science and engineering, leading to better understanding, prediction, and control of dynamic processes. In this paper, we review the role played by differential equations in data analysis. More specifically, we consider the intersection between differential equations and data analysis in the light of modern statistical learning methodologies. This article is categorized under: Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data Statistical and Graphical Methods of Data Analysis > Nonparametric Methods Statistical Models > Nonlinear Models.
| Original language | English |
|---|---|
| Article number | e1534 |
| Journal | Wiley Interdisciplinary Reviews: Computational Statistics |
| Volume | 13 |
| Issue number | 6 |
| DOIs | |
| State | Published - 1 Nov 2021 |
Bibliographical note
Funding Information:The author thanks two anonymous referees, and the co-editor for very useful comments that improved the presentation of the paper. The author also thanks Shota Gugushvili, Benjamin Reiser, Eberhard Voit, Ernst Wit, and Rami Yaari for useful discussions, and important insights.
Publisher Copyright:
© 2020 Wiley Periodicals LLC.
Keywords
- data science
- differential equations
- dynamical system
- statistical inference
- statistical learning
- time series
ASJC Scopus subject areas
- Statistics and Probability