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🪁 A fast Adaptive Machine Learning library for Time-Series, that lets you build, deploy and update composite models easily. An order of magnitude speed-up, combined with flexibility and rigour. This is an internal project - documentation is not updated anymore and substantially differ from the current API.
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chore(Release): Bump version from 2.0.0 -> 2.0.1
106125aView on GitHubchore(Release): Bump version from 1.0.9 -> 2.0.0
172c6faView on GitHubfix(Loop): backtesting calculate `memory_size` correctly
da09502View on GitHubfeature(Loop): added `trim_initial_period_after_preprocessing` option, set disable_memory to `True` by default (#532)
99d1ab7View on GitHubchore(Release): Bump version from 1.0.8 -> 1.0.9
b7639a5View on GitHubfix(Loop): also added `project_hyperparameters` to `TrainedPipelineCard` (#531)
d4f0fabView on GitHubchore(Release): Bump version from 1.0.7 -> 1.0.8
1033825View on GitHubfeature(Loop): added & propagate new `project_hyperparameters` property to Composite.Metadata (#530)
c454fa8View on GitHubfeature(Models): omit feature importances in Artifact during `.fit()` (#529)
79daa66View on GitHubchore(Release): Bump version from 1.0.6 -> 1.0.7
2e4c576View on GitHubfix(Inference): Metadata's name is unified across training/inference (#528)
7f9613cView on GitHubchore(Release): Bump version from 1.0.5 -> 1.0.6
9c2652cView on GitHubfix(Inference): set Composite.Metadata correctly for preprocessing pipeline (#527)
7596419View on GitHub