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)