Overview

Incremental training

Estimators are now re-trainable on new data, using the train_from method. This accepts a previously trained model (predictor), and new data to train on, and can greatly reduce training time if combined with early stopping. The feature is integrated with gluonts.shell-based SageMaker containers, and can be used by specifying the additional model channel to point to the output of a previous training job. More info in #2249.

New models

Two models are added in this release:

• DeepVARHierarchicalEstimator, a hierarchical extension to DeepVAREstimator; learn more about how to use this in this tutorial.
• DeepNPTSEstimator, a global extension to NPTS, where sampling probabilities are learned from data; learn more on how to use this estimator here.

Deprecated import paths and options

This release moves MXNet-based models from gluonts.model to gluonts.mx.model; the old import paths continue working in this release, but are deprecated and will be removed in the next release. For example, now the MXNet-based DeepAREstimator should be imported from gluonts.mx (or gluonts.mx.model.deepar).

We also removed deprecated options for learning rate reduction in the gluonts.mx.Trainer class: these can now be controlled via the LearningRateReduction callback.

Dataset splitting functionality (experimental)

We updated the functionality to split time series datasets (along the time axis) for training/validation/test purposes. Now this functionality can be easily accessed via the split function (from gluonts.dataset.split import split); learn more about this here.

This feature is experimental and subject to future changes.

Changelog

Breaking changes

• Breaking: Update data splitters to return (input, output) pairs in the test split (#2031 by @npnv)
• Breaking: Move MXNet-based models to mx.model. (#2126 by @Hongqing-work)
• Convert time-features into functions. (#2149 by @jaheba)
• Remove deprecated args from mx.Trainer. (#2153 by @jaheba)
• Reduce sdist size. (#2199 by @jaheba)
• Remove core.exception module. (#2202 by @jaheba)
• Remove core.ty. (#2203 by @jaheba)
• Update gluonts.dataset.split code, test, docs (#2223 by @lostella)
• Remove gluonts_forecasters entrypoint mechanic. (#2278 by @jaheba)
• Enable 'python -m gluonts'. (#2292 by @jaheba)

New features / major improvements

• Interrupting mx.Trainer stops training. (#2131 by @Hongqing-work)
• Expose evaluator aggregation_strategy functions (#2198 by @kashif)
• Add data preparation utility for hierarchical time series and a tutorial notebook (#2206 by @rshyamsundar)
• Add Deep NPTS model (#1835 by @rshyamsundar)
• Improve arrow reading performance. (#2217 by @mr-1993)
• Allow DeepVAR model to use (global) dynamic features (#2226 by @rshyamsundar)
• Hierarchical: Allow use of external dynamic features and add a section in the tutorial (#2253 by @rshyamsundar)
• Add serde.dataclass. (#2166 by @jaheba)
• R: Add Python wrapper for calling R's hierarchical methods (#1685 by @rshyamsundar)
• Add learning rate and weight decay arguments to PyTorch estimators (#2289)
• Added LR scheduler to DeepAR Pytorch (#2287 by @shubhamkapoor)
• Add LR scheduling patience option to MQF2 (#2291 by @lostella)
• Add incremental training (#2249 by @lostella)
• Add input size and type information to DeepARModel, and example_input_array to DeepARLightningModule. (#2307 by @jgasthaus)
• Add dataset.schema.translate. (#2304 by @jaheba)
• Add forecast_start to entry-wise metrics in evaluator (#2312 by @lostella)

Bug fixes / minor improvements

• Fix DatasetCollection (#2135 by @rsnirwan)
• Fix PandasDataset for Python 3.9 (#2141 by @lostella)
• Make PandasDataset faster (#2148 by @lostella)
• Ignore divide warnings in evaluation. (#2159 by @jaheba)
• Fix Prophet wrapper to work with Timestamp instead of Period (#2182 by @lostella)
• Fix dtype for "item_id" column in metrics dataframe (#2183 by @lostella)
• Fix recursive case for gluonts.mx.batchify.stack (#2184 by @lostella)
• Fix item_id values in ConstantValuePredictor (#2192 by @codingWhale13)
• Fixup Patience class. (#2197 by @jaheba)
• Fix dataset arrow writer tool. (#2196 by @jaheba)
• Fix SymbolBlock serde issue (#2187 by @lostella)
• Add item id to Uber TLC dataset (#2214 by @mvanness354)
• Fix r_forecast wrapper to shift start date when truncating time series (#2216 by @abdulfatir)
• Fix dtype bug in piecewise_linear and add a test (#2224 by @rshyamsundar)
• Fix bug in to_quantile_forecast (#2225 by @eugeneteoh)
• Fix gluonts.mx.trainer.Trainer in case of empty data loader (#2228 by @lostella)
• Fix feed-forward models when features are provided (#2238 by @lostella)
• update SplicedBinnedPareto demos from nursery version to gluonts version (#2250 by @elenaehrlich)
• Improve len() for ParquetFile. (#2261 by @jaheba)
• Move max_idle_transform usage to GluonEstimator. (#2262 by @jaheba)
• Optimize TimeSeriesSlice performance (#2259 by @lostella)
• Fix ignore hidden files when generating datasets (#2263 by @kashif)
• Fix: set max idle transforms in PyTorch estimators (#2266 by @lostella)
• Fix QuantileForecast.plot() to use DateTimeIndex (#2269 by @abdulfatir)
• Fix serde dataclass eventual. (#2277 by @jaheba)
• Fix gluonts.dataset.split for multivariate case (#2314 by @lostella)
• Improve TestData class in gluonts.dataset.split (#2315 by @lostella)
• Simplify make_evaluation_predictions (#2309 by @lostella)
• Fix MQCNN for kernel_size=1 (#2321 by @lostella)
• Simplify unbatching in forecast-generator. (#2334 by @jaheba)
• Fix numerical bug in BinnedUniforms (#2344 by @moudheus)

Documentation

• Docs: Make notebook templates. (#2122 by @jaheba)
• Docs: Rework installation section. (#2130 by @jaheba)
• Docs: Fix running tutorials for publishing docs. (#2138 by @jaheba)
• Docs: Update hyperparameter tuning with optuna notebook. (#2137 by @npnv)
• Fix issues with hyperparameter tuning tutorial (#2143 by @lostella)
• Apply black to notebooks. (#2144 by @jaheba)
• Docs: Simplify wide DataFrame example (#2150 by @lostella)
• Docs: fix links in models table (#2156 by @lostella)
• Add 'Background' section to docs. (#2129 by @jaheba)
• Docs: Add info about version guarantees. (#2161 by @jaheba)
• Docs: fix tutorial after breaking changes in trainer class (#2179 by @lostella)
• Add tutorial with data splitting examples (#2157 by @npnv)
• Fix: add missing link to splitting tutorial (#2185 by @lostella)
• Fix: ensure last cell of tutorials runs (#2186 by @lostella)
• Fixes to the dataset splitting tutorial (#2189 by @npnv)
• Update TSBench readme with paper reference (#2191 by @geoalgo)
• Update Available models table with the hierarchical model (#2209 by @rshyamsundar)
• Fix broken links in Available-models table (#2211 by @rshyamsundar)
• Add logo to README. (#2248 by @jaheba)
• New logo. (#2243 by @jaheba)
• Use brand colors in docs. (#2257 by @jaheba)
• Docs: Reformatting table, badge colors. (#2258 by @jaheba)
• Docs: update contribution guidelines and dev setup (#2270 by @lostella)
• Add Github footer icon to docs. (#2285 by @jaheba)
• Docs: Custom Pygments style for dark theme. (#2290 by @jaheba)
• Fix README quick examples (#2297 by @lostella)
• Fix text in Quick Start Tutorial (#2300 by @sighellan)
• Update README and tutorial (#2311 by @lostella)
• Turn on apidoc generation (#2332 by @jaheba)
• Add info on how to use 'just' (#2339 by @codingWhale13)
• Small documentation improvements (#2343 by @codingWhale13)

Test / setup changes

• add python 3.9 to test workflows (#2136)
• Tests: Move mx model test. (#2158 by @jaheba)
• Test: Use spawn method for shell server tests. (#2177 by @jaheba)
• Remove holidays and matplotlib from core dependencies. (#2055 by @jaheba)
• Update minimal version for nbconvert. (#2233 by @jaheba)
• Hierarchical: Add a test for to_dataset method (#2265 by @rshyamsundar)
• Fix mypy and black commands in pre-commit githook (#2271 by @abdulfatir)
• Update project_urls. (#2274 by @jaheba)
• Move _version to meta. (#2293 by @jaheba)
• Remove setup-requires. (#2295 by @jaheba)
• Remove pytest.ini. (#2298 by @jaheba)
• Speed up smoke tests (#2341 by @lostella)