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)