Support uncertainty in timeseries values
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Description
It has come up in the first post on scenario modelling, that support for uncertainty in modelling assumptions and timeseries values would be good. I'll keep a running table here of use cases before implementing a solution. There are a lot of ways this could be done I expect, and various pros cons (e.g. tracking upper and lower bounds, Z values, arbitrary probability distributions per timeseries value, a distribution family per timeseries with different parameters per value element, temporal uncertainty vs. value uncertainty, just using Pint's Measurement object, whatever it does)
Use cases:
- BovaerMonitoring: guessing costs of administrative overhead and on-site inspection
Contributor guide
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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
No files, tests, or entry points are named. Start by reviewing the scenario-modelling context and the BovaerMonitoring use case, then compare the uncertainty approaches listed in the issue; done requires an agreed design for representing uncertainty in modelling assumptions and timeseries values.
Written by the indexing model from the issue text.
Assessment
- Domain
- data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100