tensorflow / tensorflow/probability
Modelling an ARCH process
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enhancement
question
- Dominant language
- Jupyter Notebook
- Stars
- 4.4k
- Forks
- 1.1k
- PR merge metrics
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Description
An AR processes can be modelled with tfp.sts.Autoregressive.
How would one add a conditional heteroskedasticity component to a model?
If I receive a hint, I can perhaps make a notebook for future reference!
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 with the tfp.sts.Autoregressive entry point and the issue's ARCH requirement. Determine whether a conditional heteroskedasticity component can be modelled there and what a notebook would need to demonstrate. Done means a concrete, documented notebook example suitable for future reference.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, tensorflow
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100