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)