pymc-devs / pymc-devs/pymc-examples
extension with plot_ts for timeseries
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- Dominant language
- Python
- Stars
- 398
- Forks
- 325
- Avg merge
- 9d 15m
- Merged PRs (30d)
- 1
Description
Notebook proposal
Title: Extension for timeseries notebooks to plot_ts for out of sample predictions
Why should this notebook be added to pymc-examples?
Because up to now all the notebooks related to timeseries and some of the examples that can be related to this only show how to fit the model and not how to make predictions after this. An idea to do so is the plot_ts function from arviz. This would help normal users like me a lot.
Related notebooks
Stochastic volatility case study, multivariate random gaussian walk ,Dependent density regression, Analysis of an AR model, Air passengers - Prophet like model, Gaussian process smoothing ....
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
Review the related timeseries notebooks listed in the issue and the ArviZ plot_ts function first. Determine which notebook should demonstrate out-of-sample predictions, then add the example so the notebook fits the model and visualizes predictions beyond the observed data.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, python
- Domain
- documentation
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100