0.4.0

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)