Question: Does Orbit support multi-instance time-series data?
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python
- Domain
- machine-learning
Research direction
Start with the README sections describing multivariate models and the iclaims dataset. Determine whether the documented APIs accept multiple store or state instances and whether they can learn correlations across them; done means a clear supported-or-unsupported answer, with any required scope identified.
Written by the indexing model from the issue text.
Description
I understand from the README that Orbit supports multi-variate models, i.e multiple regressors. I was curious to know if it supports multi-instance. For example, consider the iclaims dataset in the readme. Can it take in multiple instances of that claims data, different data for say, each US state?
As a more concrete example, consider this OJ Sales Dataset. In this case, the data contains weekly sales of orange juice over 121 weeks. There are 3,991 stores included and three brands of orange juice per store so that 11,973 models can be trained.
I understand one can train independent models for each of the stores. However, I was interested in knowing if Orbit can take in data from multiple stores to learn correlations between them.
- Dominant language
- Python
- Stars
- 2.1k
- Forks
- 147
- PR merge metrics
- No merged PRs in 30d
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