pymc-devs / pymc-devs/pymc-examples
Hurricane Path Forecasts using HSGP and Vector Auto Regression
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 398
- Forks
- 325
- Avg merge
- 9d 15m
- Merged PRs (30d)
- 1
Description
Notebook proposal
Title: Hurricane Path Forecasts
Why should this notebook be added to pymc-examples?
I think this would be an interesting example to have because of the following:
- This model is a multi-output estimation (distance + direction) problem
- It is a relevant topic with real world implications that are currently newsworthy
- It shows how one can apply/extend the very cool work depicted in example_1 by @NathanielF and in example_2 by @ricardoV94
Suggested categories:
- Level: Beginner/Intermediate
Related notebooks
Links to related notebooks mentioned above.
References
references in notebooks above + any other if relevant at completion of example.
Preliminary work
I have a good chunk of the work complete but it is still a work in progress. I am trying to gauge if this will be of interest to the PYMC community. here is a link to what I have so far.
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 reviewing the preliminary work in the linked gist and the related bayesian_var_model example, along with the referenced Bayesian vector autoregression post. Confirm the proposed hurricane path model, data, and references, then prepare a complete example notebook for pymc-examples with the stated beginner/intermediate scope.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 32/100