add support for normalizing flows + kalman filters
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- Dominant language
- Python
- Stars
- 1k
- Forks
- 114
- Avg merge
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- Merged PRs (30d)
- 1
Description
The goal is to reproduce this paper
E. de Bézenac et al., “Normalizing Kalman Filters for Multivariate Time Series Analysis,” Adv. Neural Inf. Process. Syst., vol. 33, pp. 2995–3007, 2020 [Online]. Available: https://proceedings.neurips.cc/paper/2020/file/1f47cef5e38c952f94c5d61726027439-Paper.pdf.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the linked NeurIPS paper and then inspect Dynamax's existing state-space and Kalman-filter implementation. Identify the entry points involved in adding normalizing flows and compare their behavior with the paper's experiments. Done means the paper's Normalizing Kalman Filter is reproduced in the package, with validation against the stated results.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100