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-0.6.1-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 11dc1f4e3e36c5f159c18debbe01582bafcf9d9e4def5f458813a46890727416 |
|
MD5 | f4a46574cc8a6bca5e236376b5789c9b |
|
BLAKE2b-256 | 23b15524e62e6fda10e14bc0bc94bda5b065a34efa0412c4d29ada9c20ec03c3 |