Releases: awslabs/gluonts
0.6.7
0.6.6
Backporting fixes:
0.6.5
0.6.4
0.6.3
0.6.2
0.6.1
0.6.0
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 #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)
0.5.2
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)
