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75157c628503e1868186dd6c57a340479e1792b4
* feat(Labeling): purge overlapping events, sort dataframe at loading time * fix(Linter): ran * refactor(Labeling): moved purge_overlapping_events one abstraction level higher * fix(Data): renamed class * fix(Data): corrected parameter name * fix(Config): parameters * fix(Data): fixed path * fix(Data): uncommented required code * feat(EventFilters): use vol based CUSUM * fix(Config): only retrain every 2000 samples * fix(Config): filter out even more events * fix(Inference): added remove_overlapping_events * refactor(Types): simplified type hierarchy
feat(Project): use SKLearn models directly, removed custom ensembling, use 5 minute data, batch inference, numba cusum filter (#192)
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%