gluonts 0.13.0 on Python PyPI

Overview

We're happy to release gluonts version 0.13! This release contains a few new features and breaking changes compared to 0.12, especially around PyTorch models, data handling and model evaluation:

Added Support for PyTorch 2.0 , Lightning 2.0, Pandas 2.0.
New, more ergonomic model evaluation routines were added in #2673 (see the PR description for details on how to use those).
New PyTorch-based models, including PatchTST (#2748), and other torch-models-related breaking changes and improvements like #2603, #2614, #2628, #2688, and #2618.
Faster PandasDataset (#2663, #2665, #2860)

There are several more improvements and fixes compared to 0.12, which you can find in the changelog below. This release was possible thanks to the great work of several contributors: @jaheba, @MarcelK1102, @lostella, @gorold, @kashif, @dcmaddix, @abdulfatir, @melopeo, @huibinshen, @shchur, @pablovicente, @Gandor26, @Linbo-Liu. Thanks everyone, and thanks to users and authors of issue reports for the precious feedback!

Changelog

Breaking changes

Add transform.Valmap, improve transform.Chain. (#2629)
make Pytorch scaler's forward API consistent (#2627)
Remove torch specific Dataloader, remove num_workers from torch models. (#2628)
Pass prediction inputs as dict. (#2646)
Simplify scalers, move to gluonts.torch.scaler (#2632)
Remove TimeSeriesSlice (#2680)
Add model.Input. (#2684)
Remove FallbackPredictor. (#2686)
Move model init to lightning module. (#2688)
torch.SimpleFeedForward: Rename context to past_target. (#2704)
Fix style and type issues (#2711)
Fix off-by-one in torch DeepAR (#2618)
Remove dataset.Schema. (#2798)
Simplify univariate R wrapper (#2830)
Simplify plotting of forecasts. (#2864)

Major improvements or new features

Expose weight_decay in torch TFT estimator class (#2603)
Allow ReduceLROnPlateau to track val_loss when validation set is available (#2614)
Add wrapper for Nixtla/hierarchicalforecast (#2591)
Add zebras freq/period. (#2651)
Faster index building in PandasDataset (#2663)
Speed up PandasDataset.from_long_dataframe (#2665)
Add zebras.TimeFrame. (#2672)
Allow PyTorch 2.0 (#2724)
Fix Pandas 2.0 compatibility issues (#2710)
Allow PyTorch Lightning 2.0 (#2728)
Add itertools.PickleCache. (#2756)
Add Monash repository datasets (#2771)
Merge zebras from proof of concept branch. (#2776)
Add interface to gluonts.ev (#2673)
Zebras: Add from_pandas classmethods to TimeFrame and Periods. (#2807)
Zebras: Improve TimeFrame.split. (#2808)
Zebras: Add TimeFrame.rename. (#2810)
Zebras: Add TimeFrame.rolsplit. (#2809)
Add fourier.arima for long seasonal time series (#2789)
Add patch-TST, D-Linear and a new lag-TST model (#2748)

Minor improvements or new features

Rework torch MeanScaler. (#2600)
Add dataset.loader.as_stacked_batches. (#2638)
Add Cyclic.stream. (#2639)
Add Wiki 2000 dataset (#2642)
Make hierarchicalforecast a single module. (#2666)
Add itertools.pluck_attr. (#2668)
Add itertools.power_set (#2682)
Allow axis to be a tuple in ev.aggregations (#2681)
Export torch models in torch module directly. (#2685)
Add zebras resize. (#2705)
improve comprehension list style (#2715)
Improve check for validation loop in lightning modules (#2726)
Add warning for "object" features in PandasDataset (#2731)
Remove usage of glide in tsf-reader. (#2737)
Add cdf and icdf to torch NegativeBinomial distribution (#2749)
Reduce depth of get_dataset import (#2796)
Reduce depth of gluonts.dataset.repository imports in code and docs (#2803)
Fix: remove multiprocessing from TSFReader (#2806)
Add maybe stub file. (#2811)
Add SizedIterableSlice, an IterableSlice that supports len() (#2815)
add validated to DeepNPTS (#2823)
Refactor base metrics computation in Evaluator class (#2825)
Remove second call to create_lightning_module on torch estimator (#2834)
Ingore hidden files in FileDataset by default. (#2847)
Add --compression argument to arrow writer cli. (#2848)
Zebras: handling of weekday offsets. (#2849)
Zebras: Add unix_epoch method to Period and Periods. (#2851)
Zebras: Improve equality for Periods. (#2857)
Add join_items to itertools. (#2859)
Zebras: Add eq_to to TimeFrame. (#2858)
Zebras: Add eq_shape to TimeFrame. (#2863)
Zebras: Allow to slice TimeFrame using plain strings for time info. (#2865)
Zebras: Add TimeSeries.to_numpy. (#2866)
Cache groupby result in PandasDataset (#2860)
Add feat_static_cat for TSF datasets (#2871)

Bug fixes

Ensure dtype on feat_time in torch DeepAR. (#2596)
Add assertion to split function ensuring valid windows (#2587)
Fix bug with static cardinalities in PandasDataset (#2599)
Add gluonts.util.safe_extract (#2606)
Expose aggregation method in ensemble NBEATS, fix forecast shape (#2598)
Fix incorrect import in tsbench, apply latest black (#2613)
Fix: torch PoissonOutput scaling (#2619)
Remove dataclasses requirement (#2623)
Implement equals for init_passed_kwargs. (#2630)
Fix bugs in MeanScaler (#2633)
Fix validation_data usage in torch. (#2643)
Fix norm-freq to consider freq starts. (#2645)
Fix call to extractall (#2648)
Delay instantiation of ScipyStudentT object (#2660)
Fix DateSplitter when split date is before start (#2670)
Remove creation of ragged sequences in MultivariateGrouper (#2671)
Fix ev.seasonal_error (#2696)
Fix zebras period time features. (#2700)
Update hierarchicalforecast for new release (#2709)
Fix DistributionForecast failure on GPU (#2714)
Fix version location for sdist. (#2729)
Fix validation loop check for Lightning modules (#2733)
Fix version cmdclass handling. (#2735)
Fix: use non-strict inequality in definition of coverage (#2738)
Fix MXNet NOPScaler (#2744)
Fix dataset file discovery. (#2777)
Fix: Loading of nested paths in FileDataset. (#2779)
Prophet: Pass 'item_id' and 'info' to forecast. (#2780)
Avoid zero scale in StudentTOutput (#2791)
Zebras: time length fixes. (#2799)
Remove .to_timestamp() to fix interval plotting (#2800)
Fix pd.Period serialization (#2827)
Fix torch DeepAREstimator in case context_length=1 (#2841)

Documentation

Fix installation docs, fix typos in docstrings (#2625)
Fix r-forecast doc strings (#2669)
Add doctests. (#2683)
Fix MultivariateEvaluator docstrings (#2693)
Docs: minor spelling fix (#2701)
Docs: Add extra requirements. (#2732)
Update Available Models (#2740)
Update REFERENCES.md (#2824)
Update plotting in readme example. (#2867)
Docs: Fix and simplify tutorials. (#2869)
Docs: Fix black formatting of % instructions. (#2870)
Docs: Add download link to notebooks. (#2872)
Docs: Use torch DeepAR in README. (#2874)

Test / setup changes

Fix version in requirements to comply with stricter setuptools. (#2604)
Test: Increase timeout for xgboost tests. (#2601)
Ignore warnings in tests. (#2620)
Test: Remove -v option from pytest. (#2631)
Add hierarchicalforecast to github workflows. (#2659)
Make nursery tests opt-in. (#2667)
Add test for torch models tracing (#2658)
Relax pandas requirement to include pandas 2.x. (#2713)
Update CP-Flow fork as extra dependency for MQF2 (#2727)
Add scipy requirement (#2745)
Fix pandas removed deprecations in tests (#2778)
Test: Set caplog level for shell tests. (#2786)
Bump numpy from 1.19.2 to 1.22.0 in /src/gluonts/nursery/daf (#2787)
Update setuptools and wheel in test workflow (#2802)
Bump torch from 1.6.0 to 1.13.1 in /src/gluonts/nursery/daf (#2788)
Add test workflow for R based models (#2814)
Add tests for hierarchical model to R workflow (#2819)
Move notebook compilation logic to docs workflow (#2831)
Fix bug in docs workflow (#2836)
Fix docs workflow further (#2837)
Fix string literal in docs workflow (#2839)
Update action to configure AWS credentials (#2873)

Others

Move NPTS back to gluonts.model (#2597)
Remove mxnet from default dataset path (#2635)
Roll back MQF2 import (#2687)
add DAF source code (#2769)
Add code for multivariate attack paper (#2697)
Update wiki2k tarball path (#2805)
[Nursery] CoP-DeepAR: Model for temporal hierarchical forecasting (#2812)
Cop deepar: Reset the no. of epochs to the default value (#2813)
Guard scripts execution in nursery (#2832)