Gluonts

* Fix pandas version to `1.0.x`.

Changelog

New features

* Dirichlet Multinomial distribution (482)
* Datasets from the GP-Copula paper (476)
* Marginal CDFtoGaussianTransformation (486)
* DeepVAR model (491)
* GP-Copula model (497)
* Add transform objects for temporal point processes (341)
* Added operator to allow for easier chaining of transformations. (505)
* Gamma distribution implemented. (502)
* Beta distribution implemented. (512)
* Sagemaker SDK Integration (444, 585)
* Add `loc` argument to distribution output classes (540)
* Shopping holidays (542)
* Add Poisson distribution (532)
* N-Beats model (553, 588, 655)
* Support slicing of distributions (645)
* Naive2 model and OWA evaluation metric (602)
* Add LSTNet (596, 700, 791, 804)
* Data loading utils for M5 competition datasets (716)
* Add MAPE to evaluator (725)
* Add label smoothing to binned distribution (731)
* Multiprocessing data loader. (689, 739, 747, 759, 742)
* Add Categorical Distribution (746)
* Added multiprocessing support for evaluation. (741)
* Add variable length functionality to DataLoaders (780)
* Add axis option to Scaler classes (790)
* Add lead_time to predictors and estimators (700)
* Add logit normal distribution (811)

Bug fixes

* Fix instance splitter issue with short time series (533)
* Fixed distribution sampling issues. (526)
* Fix quantile of Binned distribution (536)
* Fixed FileDataset SourceContext (538)
* Fix quantile fn for transformed distribution (544)
* Fix bug in cdf method of piecewise linear distributions (564)
* Fixed taxi dataset cardinality (552)
* Fix item_id field in provided datasets (566)
* Fix Dockerfile to use Python 3.7. (579)
* Fix DeepState trend model to work in symbolic mode (578)
* Fix for symbol block serialization issue (582, 591)
* Fixed LSTNet implementation (586, )
* Fix mean_ts method of Forecast objects (624)
* Fix r-forecast package on windows. (626)
* Fix forecast index bug, add test (644)
* Fix the sign method of affine transformation (613)
* Fixing context when converting to symbol block predictor (651)
* Fix data loader and include validation channel in test (680)
* Fix incompatible date_range and matplotlib register in pandas v1.0 (679)
* Fix binned distribution for mxnet 1.6 (728)
* Remove asserts on loc and scale (734)
* Fix default scaler in seq2seq models (745)
* Fix pydanitc `create_model` usage. (768)
* Fix feature slicing in WavenetSampler (770)
* Fix bug with iteration over datasets (787)
* Use forecast_start in RForecastPredictor (798)
* Fix negative binomial's scaling (719, 814)

Breaking changes

* Moved gp module to be part of gp_forecaster. (572)

Other changes and improvements

* Changed FileDataset to be more easily inheritable. (498)
* Added strategies for timezone information. (500)
* Split up transform into its own module. (499)
* Distribution dependent loss masking. (534)
* Remove dataset class in favor of alias (560)
* Clean up lifted operations, add pow operation (571)
* Removed expand_dims when reading in time-series values. (574)
* Updated dependency to Pandas v1.0 (576)
* Refactored DataLoader. (619)
* Refactored instance sampler. (648)
* Log epochs in trainer (676)
* Improve trainer handling of learning rate scheduling and logging (701)
* Upgrade to mxnet 1.6 (709)
* Moved model tests into their own folders. (727)
* Refactor wavenet model (743)
* Disable TQDM when running on SageMaker. (810)

Changelog
* Fix that allows GluonTS to work with the latest pydantic v1.5 (783)

* Fix WaveNet prediction length during training (347)
* Relax requirements constraints (456)
* Added aggregation functionality to MultivariateEvaluator (459)
* Removed unused static method in DeepARNetwork (460)
* Updated pydantic to version 1. (465)
* Fix use of numpy.histogram. (472)
* Fix validation error in transformed distribution (475)
* Refined doc requiremnents; using sphinx 2. (477)

* Added median as alias for p50 to Forecast. (450)
* Use validation to prevent overfitting (378)
* Fix deepstate serialization, add tests (445)
* Fix escaping of string in serde.dump_code. (439)
* Added multivariate grouper and tests (432)
* Fixes to setup.py to make it work on Windows (433)

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)