Gluonts

Models

* Added Deep State model. (229)
* Added Deep Factor model. (271)
* Fixed bug when changing default activation function in WaveNet (299)
* Option for DeepAR and DeepState to allow an embedding vector instead of the same value for all categorical features. (315)
* Add option for feat_static_real in DeepAREstimator. (324)
* Fixed DeepState samples tensor shape. (340)
* Added support for changing dataytpe in DeepAREstimator. (363)
* Made cardinality argument compulsory in DeepStateEstimator. (413)
* DeepStateEstimator: Some adjustments to hyperparameter settings. (415)

Distributions

* Include quantile method in distribution. (314)
* Added slice_axis methods to Distribution. (397)
* Added Dirichlet distribution. (417)

Other new features

* Added more operators for synthetic data generation. (286)
* Included DistributionForecast and make plot generic. (316)

Bug fixes

* Updated lag error message. (266)
* Fix mistake in notebook. (269)
* Fix pandas warnings in dataset generation. (270)
* Fix numerical issue with negative binomial distribution. (288)
* Fixes fieldname issues. (292)
* Fixed a wrong reshaping in DeepAR estimator. (330)
* Small fixes to Box-Cox transformation. (349)
* Improve BinnedDistribution. (350)
* Small fix for binned distribution. (352)
* Assure Learning Rate Scheduler does not increase the learning rate. (359)
* Fix dim and copy_dim methods in SampleForecast. (366)
* Fixed the logging of the number of parameters during training. (386)
* Fix empty time_features issue. (387)
* Fix batch shape in Binned Distribution (406)
* Fix bug in multivariate Gaussian. (407)
* Fix edge case in evaluation where prediction length is 1 and prediction target is nan. (422)

Other changes

* Make item_id field uniform across predictors. (268)
* Added Dockerfile. (285)
* Pytest-timeout==1.3; removes warnings from logs. (306)
* Flask~=1.1; removes some warnings. (307)
* Make tensors and distributions serializable. (312)
* Added SageMaker batch transform support. (317)
* Manage mxnet context when deserializing predictors. (318)
* Add missing time features for business day frequency. (325)
* Switched to timestamp alignment from rollback to rollforward. (328)
* Adding GPU support to the cholesky jitter and eig tests. (342)
* Adding GP example on synthetic dataset with built-in plotting. (343)
* Introduced ForecastGenerator to wrap mxnet output into forecast object. (348)
* Add synthetic data generation tutorial. (356)
* Added pd.Timestamp to serde. (357)
* Using custom SerDe methods for deserializing params in Sagemaker. (364)
* Fixes for serializing sets and numpy numbers in SerDe. (368)
* Store GluonTS Version with stored model (388)
* Dockerfile for GPU container. Fix for installing GPU version of MXNet. (403)
* Added debug option to batch-transform. (404)
* Use static categorical feature in benchmark_m4. (410)
* Remove dataset.validate. (412)
* Renamed num_eval_samples to num_samples. (421)
* Remove mxnet requirement. (429)

* 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)

* 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)

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.

* Updated shell.

* Exclude MXNet 1.5.* from allowed requirements

* Added transformer model, tests and evaluations

* Minor improvements, changes and fixes.

* Changed shell metrics to be similar to SageMaker DeepAR.