https://github.com/amazon-science/causal-forecasting
Science Score: 26.0%
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○CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
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○DOI references
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (6.3%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: amazon-science
- License: apache-2.0
- Language: Jupyter Notebook
- Default Branch: main
- Size: 421 KB
Statistics
- Stars: 13
- Watchers: 3
- Forks: 3
- Open Issues: 1
- Releases: 0
Metadata Files
README.md
This is the code to reproduce the experiments for the paper 'Causal Forecasting: Generalization Bounds for Autoregressive Models'.
Prerequisites
Python 3
How to run
To install necessary dependencies call
pip3 install -r requirements.txt
Simulation Experiments
Run the simulation experiments with
cd simulation_experiments
python3 compare_estimators.py
You can also use different parameters, e.g.
python3 compare_estimators.py --seed=1
For a comprehensive list of parameters use the help flag.
The results will be saved in the data directory. To plot the results run
python3 correlation_vs_error_plot.py
python3 corr_vs_err_omega.py
python3 error_diff_hist.py
python3 error_stat_vs_causal.py
python3 sample_size_vs_error.py
python3 plot_corr_vs_err_misspec.py
Each file corresponds to a figure in the paper.
The resulting plots will also be stored in the img directory.
Real Data Experiments
Train the models remotly by using the script
cd gluonts
./scripts/launch_remote_jobs.sh
Then the plots from the paper can be created with the notebooks
gluonts/gluonts.ipynb
gluonts/remote-jobs-gluonts.ipynb
License
This project is licensed under the Apache-2.0 License.
Owner
- Name: Amazon Science
- Login: amazon-science
- Kind: organization
- Website: https://amazon.science
- Twitter: AmazonScience
- Repositories: 80
- Profile: https://github.com/amazon-science
GitHub Events
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- Watch event: 2
Last Year
- Watch event: 2
Issues and Pull Requests
Last synced: over 1 year ago
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- Total pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 3
Past Year
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- Pull requests: 1
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- Bot pull requests: 1
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