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522757c2823708afac5ac98a84f787e45544f4ae
* feat(CI): push to the public repo without the private repo's history * fix(CI): workflow syntax * fix(CI): use the destination ssh key * fix(CI): wrong syntax * fix(CI): taken out unnecessary step * fix(CI): correct pipe syntax? * fix(CI): create dictionary * fix(CI): use global ssh config * fix(CI): git config can be only called in a repo's dir * fix(CI): clone the repo with ssh * fix(CI): added ssh key when clonign * fix(CI): don't build the docs on PRs * fix(CI): use the correct ssh keys * fix(CI): don't push to the local brunch * fix(CI): missing chmod * chore(CI): print out private key * fix(CI): echo * Update copy_repo.yml * Update copy_repo.yml * Update copy_repo.yml * Update copy_repo.yml * Update copy_repo.yml * Update copy_repo.yml * Update copy_repo.yml * Update copy_repo.yml * Update copy_repo.yml * Update copy_repo.yml * Update copy_repo.yml * Create purge.yaml * Update purge.yaml * Update copy_repo.yml * Update copy_repo.yml * Delete purge.yaml
fix(MetaLabeling): previously misinterpreted meta-labeling, now also multiplying base model's prediction with the meta model's prediction (#193)
fix(MetaLabeling): previously misinterpreted meta-labeling, now also multiplying base model's prediction with the meta model's prediction (#193)
fix(MetaLabeling): previously misinterpreted meta-labeling, now also multiplying base model's prediction with the meta model's prediction (#193)
fix(MetaLabeling): previously misinterpreted meta-labeling, now also multiplying base model's prediction with the meta model's prediction (#193)
fix(MetaLabeling): previously misinterpreted meta-labeling, now also multiplying base model's prediction with the meta model's prediction (#193)
fix(MetaLabeling): previously misinterpreted meta-labeling, now also multiplying base model's prediction with the meta model's prediction (#193)
Financial time series prediction
And end-to-end pipeline to train predictive Machine Learning models on financial (non-stationary, regime changing) time series. Includes feature selection and meta labelling.
Description
Languages
Python
85%
Jupyter Notebook
15%