Causal Discovery Algorithms For Time Series Data
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- Dominant language
- Python
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Description
Description:
I am planning to implement various causal discovery methods for time series data. The methods I am particularly interested in include CDAN, ACD, TiMINo, and NTS-NOTEARS. Each of these methods offers unique approaches and advantages for uncovering causal relationships in time series datasets.
Methods of Interest:
CDAN (Causal Discovery with Additive Noise Models)
ACD (Auto Regressive Causal Discovery)
TiMINo (Time Series Interventions with Models for Interventions)
NTS-NOTEARS (Nonlinear Time Series with NOTEARS)
Reference:
For a detailed comparison and discussion of these methods, please refer to the paper available here.
Thank you in advance for your valuable input!
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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
The issue names no target files, tests, or entry point. Start by reading the linked comparison paper and surveying the repository for existing time-series and causal-discovery implementations. Done would require an agreed scope and implementation of CDAN, ACD, TiMINo, and NTS-NOTEARS, with corresponding validation, but those acceptance details are not provided.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100