Sane handling of time series data for forecast modelling - with production usage in mind.
Project description
Sane handling of time series data for forecast modelling - with production usage in mind. While modelling time series data with data science libraries like Pandas, statsmodels, sklearn etc., dealing with time series data is cumbersome - timetomodel takes some of that over. Loading data, making train/test data, feeding data into rolling forecasts… Also, the context and assumptions under which a model was made and used should not be in notebooks, they should have a readable and reproducible spec. Timetomodel is hopefully useful while doing data & model exploration as well as when integrating or replacing models in production environments.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for timetomodel-0.5.5-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9fc6c1d0ce7bdc296d1183c8e8d373570be1d14fbdcb3d31a251edeaced3babc |
|
MD5 | ae9bfa3961fc56c8e0fccf7f572e3516 |
|
BLAKE2b-256 | c24bad5df7179c6d44dedf2628a56d9aeb2953dd535b885e493d58ae6ce2937c |