Journal article
Neural ordinary differential equations for ecological and evolutionary time‐series analysis
- Abstract:
-
Inferring the functional shape of ecological and evolutionary processes from time-series data can be challenging because processes are often not describable with simple equations. The dynamical coupling between variables in time series further complicates the identification of equations through model selection as the inference of a given process is contingent on the accurate depiction of all other processes. We present a novel method, neural ordinary differential equations (NODEs), for learni...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Authors
Bibliographic Details
- Publisher:
- Wiley Publisher's website
- Journal:
- Methods in Ecology and Evolution Journal website
- Volume:
- 12
- Issue:
- 7
- Pages:
- 1301-1315
- Publication date:
- 2021-05-19
- Acceptance date:
- 2021-03-08
- DOI:
- EISSN:
-
2041-210X
- ISSN:
-
2041-210X
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1177308
- Local pid:
- pubs:1177308
- Deposit date:
- 2021-05-20
Terms of use
- Copyright holder:
- Bonnaffe et al.
- Copyright date:
- 2021
- Rights statement:
- © 2021 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
- Licence:
- CC Attribution (CC BY)
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