Gluonts

Latest version: v0.16.0

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0.3.3

* Adapted mean predictor to use random samples. (239)

* Added `predict_item` to RepresentablePredictor and adapted subclasses. (240)

* Added fallback predictor and decorator.

* Forecasts always start at the end of the whole target.

* Fix shell to have a canonical freq key in hyperparameters.

* Made `fallback` process-safe. Added ConstantValuePredictor.

* GluonTSException bypass fallback.

* Black everything. (244)

* Adding failure information to failure file. (247)

* Added error message to top of failure file. (248)

* fix the empty item list (249)

* fix the shape error of the canonical network (251)

* Fix documentation and enforce stricter doc builds (226)

* Reformatted math equations for the log_prob method of the GaussianProcess class (252)

* Fix yearly freq in process start field. (253)

* fix issue with MultivariateGaussianOutput (257)

* Fix shapes in CanonicalNetworkBase (254)

* Improvements for wavenet and some utils (262)

* Removed `get_granularity`. (265)

0.3.2

* Bump pandas version and remove timestamp workarounds (230)

* Fix num_eval_samples (232)

* Fixed backtest test. (235)

* Moved simple predictors to a distinct model folder. (237)

* fix 234: Added method to fixup non json-spec compliant floats to make the resp… (236)

0.3.1

Changes include:

* Serialize training metrics through the logger
* Improvements in the `core` package
* Minor changes in the `shell.sagemaker` package
* Add support for artificial datasets in the dataset repository
* Add `MeanPredictor` to `model.testutil`
* More flexible shell.serve API
* Add utilities for shell tests
* Added throughput logging for inference.

0.3.0

* Updated shell.

* Exclude MXNet 1.5.* from allowed requirements

* Added transformer model, tests and evaluations

* Minor improvements, changes and fixes.

0.2.3

* Changed shell metrics to be similar to SageMaker DeepAR.

0.2.2

More shell fixes

- Add proper documentation strings.
- Fix the printed script name in help output.
- Add a `force-static` train parameter that forces the
creation of a static predictor from the expected
model location.

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