Backporting fixes:

• PyTorchPredictor serde (#1086)
• Add equality operator for PytorchPredictor (#1190)
• fix pytorch predictor serde (#1194)

Backporting fixes:

• fixes in dataset mutability (#1171)
• Added item_id to NPTS and Naive2 forecasts (#1173)

Backporting fixes:

• fix chain method of Transformation (#1156)

Backporting fixes:

• Masking edge case fix (#1137)

Changelog

New Features:

Model averaging (#823)
add SampleForecast and Predictor objects for TPPs (#819)
Add temperature scaling to categorical distrubution (#792)
Representation module (#755)
New methods for missing value imputation (#843)
Add shuffling function (#873)
make distributions pickleable (#889)
Aggregate lag transformation (#886)
SimpleFeedForward to produce DistributionForecast (#870)
Added support for gzipped files. (#914)
MQCNN: Support for past dynamic features and scaling (#916)
Implemented model iteration averaging to reduce model variance (#901)
add nan support to simple feedforward model (#933)
Added timeout option for batch requests. (#931)
Implemented moving average (#926)
NanMixture: Distribution to model missing values (#913)
Added Rotbaum (#653)
DeepTPP: RNN-based temporal point processes model (#976)
Added logging of scored instances for batch-transform. (#1010)
Enable distribution output in seq2seq (#1008)
Implemented activation regularization (#955)
Adding mean absolute quantile loss to avoid the case of dividing by 0 as a possible HPO metric (#1012)
Inflated Beta Distributions (#1018)
Implement different dropout strategies (#963)
Adding support for num_forking as MQ-CNN hp (#1022)
Generalised Pareto distribution (#1031)
Added use of supported quantiles in shell when QuantileForecastGenerator is used. (#1048)
PyTorch Predictor (#1051)
Add TFT model (#962)
ConvTrans Implementation (#961)
Add evaluation metrics for anomaly detection (#1065)
Add piecewise linear quantile function output with fixed knots (#1074)
include callback in trainer and example for warm starting (#1087)
initial pytorch distribution output class (#1082)
Glide (#995)
specialize plot method for QuantileForecast (#1114)

Bug fixess

Fixed disabling of tqdm. (#839)
Fix comparison of ParameterDict when non prefixed variables are in dict. (#859)
Fixing edge case of prediction length 1. (#867)
Frequency String for Pandas Timestamp (#884)
fix imports (#885)
Fixed invalid num_worker possibility. (#892)
Corrected the formula for the stddev of the MixtureDistribution. (#900)
Fix pathes in R for Windows. (#903)
Scale the negative binomial's gamma (#909)
Shape squeeze edge case bug fix (#911)
Use of \n to split lines in batch transform. (#920)
Fixing cardinality array when use_feat_static_cat = False but feat_static_cat present in dataset (#918)
Fix batch-transform case, where request is empty. (#927)
fix DeterministicOutput, add tests (#982)
Fixing the FileDataset case with caching off for num_workers calculation (#986)
Overriding early stopping for iteration-based averaging strategies (#993)
Bug Fixes, Warnings, and One-Hot Encodings for Rotbaum (#980)
Fixing case with only time features and yearly freq (#1002)
Fixed import of Trainer. (#1005)
Fixed DeepAR typing error (#1017)
Fix sampling for MixtureDistribution class (#1042)
MQ-CNN: Bound context_length by the max_ts_len - prediction_length (#1037)
Fix gamma nans (#1061)
Fix scaling for MQ-(C|R)NN when distribution outputs are used (#1070)
added value in support to mixture output (#1077)
fix Gamma distribution's NaN gradients for zero inputs (#1078)
Fix dataset.splitter max_history argument (#1085)
Fix max window (#1097)
Ignore NaN values during training and throw a warning (training got stuck before) (#1104)
Fix a few bugs about tensor shapes in default values for TFT implementation (#1093)
Fixes awslabs/gluon-ts#1106 (#1125)

Breaking changes

Mqcnn rts (#668)
Changed dataset.splitter to use DataEntry instead of TimeSeriesItem (#890)
Refactoring data loading utilities (#898)
Removed TimeSeriesItem. (#904)
refactor imputation transformation (#907)
making backtest_metrics simpler (#924)
Moved get_seasonality from evaluation to time_feature. (#971)
Removed mxContext from core. (#977)

Other changes and improvements

Update bug_report.md (#835)
Dockerfile for R container added (#841)
Added mx module. (#876)
Adapted use of mx module. Applied isort. (#878)
Simplified AsNumpyArray. (#879)
Removed unused Transformation.estimate. (#880)
Added README to shell. (#882)
Docs requirements (#883)
add documentation related to shuffle_buffer_length/ (#910)
Default QuantileForecast.mean to p50. (#930)
Addded trimming to encoded sagemaker parameters in shell package. (#917)
Shell: Fix writing of output/failure file in case of error in provided hyper-parameters. (#942)
Pass listify_dataset as a hyperparameter through the shell (#934)
Evaluation metrics now stored in output folder (#938)
Make TrainEnv path argument explicit. (#943)
Removing mp worker del method. (#944)
Fixed logical error in data_loder tests. (#951)
Pass multiprocessing parameters through the shell (#952)
Fix pandas requirement. (#967)
Fix shell.train.
Moved Dockerfiles to examples/dockerfiles (#946)
Cleaned up unused imports. (#1007)
Fix docstrings for SimpleFeedForward (#1009)
Fix docstrings, enable distr_output in MQRNN (#1021)
Update README.md (#1024)
Update holidays version (#1033)
improved and simplified aggregate lag transformation (#1028)
Refactoring forecast generators and predictors for framework independence (#1052)
Improved logging for batch-transform. (#1059)
Reverting #1042 and adding shape assertions to the MixtureDistribution (#1058)
Using pad_to_size function to remove duplicate code in pad_arrays (#1047)
re-organized modules and imports (#1068)
speed labels_to_ranges using numba (#1071)
Fix numba warning; mask np.nan labels (#1072)
added PyTorch predictor example notebook (#1053)
refactor multiprocessing batcher to work with spawn method (#1080)
Using zero floating point tolerance in denominator rather than checkign for exact zero equality (#1079)
Fix FieldNames of Train/test splitter (#1083)
Added Stateful to serde. (#1088)
Added ty.checked decorator. (#1091)
update links in readme (#1090)
Adding test_quantiles hyperparameter to the shell to specify the quantiles for evaluation (#1096)
Refactored serde into a package. (#1100)
Refactored shell. (#1101)
Updated pytest to v5. (#1102)
cap pydantic version (#1115)
add item_id to forecast from seasonal naive (#1113)
reduce number of batches used in test (#1131)
fix pandas usage and remove version cap (#1132)

Backporting fixes:

• remove kwargs from hybrid_forward input name inference (#846)
• Fix the quantiles used to compute MSIS (#849)
• Fix to_time_series_item in splitter (#874)
• updated Pandas deprecation (#875)
• Updated Frequency String for Pandas Timestamp (#884)
• splitting features and uses DataEntry instead of TimeSeriesItem (#890)
• fix multiprocessing in backtest function (#915)
• Fix LDS distribution for the case where sequence_length==1 (#921)
• Defaulting MSIS to NaN when it can't be calculated. (#923)

• 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)

Changelog

v0.4.1 includes:

• 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)

• 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.

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.

• Fixup of shell. (#180)

• Re-added locate for Forecaster detection. (#181)

• Minor fixes.

New features

• Adding option to rescale time series instead of clipping them, in artificial dataset generation (#114)
• Include cdf method in distributions (#120, #133)
• Add support for feat_dynamic_real field in DeepAREstimator (#125)
• Add NPTS model (#148)
• Added shell package (#151, #156, #173)
• Custom seasonality for complex seasonal dataset (#155)

Other changes

• Renamed time_granularity -> freq, time_freq -> freq (#172, #173)
• Include safety check that catches infinite loops in transformations (#139)

Fixes

• Fixes to paths (#143)
• Fix default context_length in MQCNN and MQRNN estimators (#167)