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)