how to interpret the output of the ganger_lasso
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Description
Hi, thanks for the great work.
I am currently experimenting with the granger_lasso algorithm provided in the repository.
Given that one time series has N dimensions, and the time lag is equal to T.
The shape of the output of granger_lasso is [N, N * T].
May I know how I should interpret this and convert it into a [T, N, N] matrix where, along the time lag dimension, the matrix [N, N] represents the influence of the i-th node on the j-th node.
Thank you!
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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
Start by locating the repository's granger_lasso implementation and any existing documentation or examples for its output. Trace how the [N, N * T] result is assembled, then document the dimension and node/lag interpretation, including the expected [T, N, N] arrangement.
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Assessment
- Tech stack
- python
- Domain
- data, machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100