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@lostella lostella released this 12 May 16:39
v0.5.0
46e22a7
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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)
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