Releases · awslabs/gluonts

Overview

Support for Python 3.6 is dropped (#2542).

Models:

Added PyTorch implementation of the Temporal Fusion Transformer model (#2536)
Various improvements to PyTorch DeepAR (#2433, #2476, #2545, #2552, #2553, #2556, #2596)
Added wrappers for statsforecast models (#2360, #2515, #2561)
Added wrappers for hierarchical time series models in R (#2396, #2406, #2412)
Updated R wrappers and dockerfile (#2571, #2572)
Important the Naive2, Rforecast, Prophet, and Rotbaum models have been moved to gluonts.ext (#2362, #2597)

Data:

Improved PandasDataset: allows specifying static features as a separate dataframe,
instead of wastefully replicate feature values over time. This was particularly problematic
in large datasets, such as M5. In the new setup, static features are provided via
a separate dataframe indexed by item_id, and the dtype of each column determinins
which are numerical vs categorical features, with automated detection of cardinalities
in the latter case. See the updated tutorial notebook on how to use it.

Evaluation:

New evaluation module gluonts.ev (#2450) will gradually replace the existing
gluonts.evaluation as an improved, more flexible alternative.

Changelog

Breaking changes

Remove folders of models that have moved to mx.model (#2356) @codingWhale13
Remove model.common. (#2358) @jaheba
Remove dataset.rolling_dataset. (#2361) @jaheba
Remove DummyEstimator. (#2357) @jaheba
Introduce gluonts.ext. (#2362) @jaheba
Make serde.dataclass always kw-only. (#2428) @jaheba
Add copy_dim to QuantileForecast, change dim method for multivariate data (#2352) @codingWhale13
Include loss computation in torch DeepAR module, decouple MQF2 (#2476) @lostella
Remove serde dump_code/load_code. (#2482) @jaheba
Move SelfAttentionEstimator to gluonts.nursery (#2534) @lostella
Require python 3.7. (#2542) @jaheba
Simplify forecast.Quantile. (#2544) @jaheba
Move shell related forecast classes to shell. (#2547) @jaheba
Consolidate DeepNPTSEstimator (#2496) @lostella
Improve PandasDataset (#2573) @lostella @jaheba
Simplify PandasDataset. (#2583) @jaheba

Major improvements / new features

Add dict like interface for Forecast. (#2384) @jaheba
Enable dropping of columns in dataset.schema.translate. (#2387) @jaheba
Exposing the choice of train_sampler and validation_sampler for MQCNN and MQRNN (#2381) @sighellan
Add wrapper for statsforecast models (#2360) @lostella
Add dataset.schema.Schema + types. (#2391) @jaheba
Add IQN implementation (#1784) @kashif
Add hierarchical time series reconciliation methods from R/hts: top-down and middle-out (#2396) @melopeo
Add hierarchical time series reconciliation method from R/hts: MinT (#2406) @melopeo
Change schema.Type to behave like invokable types. (#2443) @jaheba
Add cdf and icdf methods for StudentT distribution (#2439) @shchur
Better DeepAR lags for business day frequency time series. (#2433) @sighellan
Add support for feather; incl compression. (#2452) @jaheba
Introduce ev module (#2450) @codingWhale13 @jaheba
Speed up PandasDataset further (#2441) @lostella
Add Empirical Risk Minimzation (ERM) hierarchical forecasting method (#2412) @melopeo
Update statsforecast model wrappers (#2515) @lostella
Add nan values and explainability support for rotbaum (#2537) @zoolhasson
Enable setting a custom imputation method in deepar pytorch (#2545) @shubhamkapoor
Add derive_auto_fields for DeepAR PyTorch (#2552) @shubhamkapoor
Add default_scale to MeanScaler and enable the option in DeepAR-PyTorch (#2553) @shubhamkapoor
Add statsforecasts models (#2561) @melopeo
Add TemporalFusionTransformer implementation in PyTorch (#2536) @shchur
Fix r_forecast methods to work with rpy2 v3+ (#2571) @abdulfatir
Updated dockerfile for R forecast models (#2572) @abdulfatir
Shell: Add support for requirements.txt files. (#2582) @jaheba
Expose weight_decay in torch TFT estimator class (#2603) @gorold
Allow ReduceLROnPlateau to track val_loss when validation set is available (#2614) @gorold

Minor improvements / new features

Expose SampleForecast, QuantileForecat directly in model. (#2366) @jaheba
Mypy fixes (#2427) @jaheba
Add nursery.pipeline. (#2429) @jaheba
itertools.select. (#2426) @jaheba
Add itertools.Filter. (#2438) @jaheba
Add itertools.trim_nans. (#2460) @jaheba
Add itertools.inverse. (#2463) @jaheba
Fix: sort dataset keys in error message when importing non-existing dataset (#2497) @lostella
Few shot forecasting (#2517) @RingoIngo
Allow passing of additional args to dataclass. (#2531) @jaheba
Simplify linear interpolation in forecast.py (#2546) @jaheba
Add util.lazy_property. (#2557) @jaheba
Compact PandasDataset string representation (#2558) @lostella
Add default args and assertions to DeepAR pytorch module, assertions (#2556) @lostella
Update MANIFEST.in. (#2566) @jaheba
Add util.copy_with. (#2562) @jaheba
Add missing value imputation to Seasonal Naive (#2569) @abdulfatir
Implement get-item for JsonLinesFile. (#2574) @jaheba
Make itertools Map/Filter dataclasses. (#2579) @jaheba
Add itertools.StarMap. (#2584) @jaheba
Add gluonts.maybe. (#2585) @jaheba
Rework maybe. (#2593) @jaheba

Bug fixes

Fix dominick dataset bug. (#2364) @haskarb
Proposed fix to zero seed bug. (#2379) @sighellan
Fix rotbaum random seed and num_samples argument. (#2408) @sighellan
Removed unused import in test.(#2409) @kashif
Hierarchical: Make sure the input S matrix is of right dtype (#2415) @rshyamsundar
Speed up PandasDataset for long dataframes (#2435) @lostella
Fix frequency inference in PandasDataset (#2442) @lostella
Fix plotting date index bug in anomaly detection example (#2446) @Amrit-Bhaskar-abhask10
Add test cases for PandasDataset, fix missing assertion (#2453) @lostella
Fix MANIFEST.in (#2456) @lostella
Fix pandas issue with inferring start of X frequency. (#2462) @jaheba
Change default forecast_type of ND metric to median (#2472) @codingWhale13
Fix: use right context in DeepVARHierarchicalEstimator (#2478) @c3-ziqin
Add requirement files to MANIFEST.in (#2490) @jaheba
Fix dataclass handling of member inheritance. (#2492) @jaheba
Fix DateSplitter for multiples of base frequencies (#2500) @lostella
Fix serde.dataclass inheritance handling. (#2512) @jaheba
Fix QuantileForecast.quantile in case only mean is stored (#2513) @lostella
Fix aggregate_valid for non-numerical columns (#2526) @lostella
Fix dataclass eventual handling. (#2530) @jaheba
Change SeasonalNaive fallback predictor to nanmean (#2549) @abdulfatir
Fix: add missing params in rotbaum (#2554) @zoolhasson
Add NaN validation to Evaluator (#2568) @abdulfatir
Fix: avoid automatic device detection via serialized tensors when deserializing (#2576) @shubhamkapoor
serde: Fix encoding of dtypes. (#2586) @jaheba
Fix bug with static features in PandasDataset (#2589) @lostella
Fix maybe map_or/map_or_else return types. (#2588) @jaheba
Add assertion to split function ensuring valid windows (#2587) @MarcelK1102
Ensure dtype on feat_time in torch DeepAR. (#2596) @jaheba
Expose aggregation method in ensemble NBEATS, fix forecast shape (#2598) @lostella
Fix bug with static cardinalities in PandasDataset (#2599) @lostella
Add gluonts.util.safe_extract (#2606) @lostella
Fix incorrect import in tsbench, apply latest black (#2613) @lostella

Documentation

Udpate DeepAR import in README. (#2359) @codingWhale13
Remove strange quoting marks from docstrings (#2368) @lostella
Change 'confidence interval' to 'prediction interval' (#2373) @codingWhale13
Fix use of dump_code in tutorial. (#2488) @jaheba
Fix docstrings according to docformatter (#2501) @lostella
Docs: Fix install instructions. (#2508) @jaheba
Add examples to docstring for periods_between (#2504) @lostella
Add info on how versioning works. (#2529) @jaheba
Improve README example (#2538) @lostella
Update REFERENCES.md @dcmaddix

Test / setup changes

Update workflow actions to latest versions (#2447) @lostella
Tests: Change Python versions. (#2448) @jaheba
Use ruff instead of flake8. (#2485) @jaheba
Apply ruff/pyupgrade to test. (#2489) @jaheba
Add smoke tests for torch models (#2495) @lostella
Pin docformatter version. (#2507) @jaheba
Cap numpy compatibility in mxnet extra requirements (#2506) @lostella
Clean up test code for evaluator (#2505) @lostella
Remove mypy plugin for dataclass. (#2514) @jaheba
GH Actions: Use authenticated requests for just. (#2522) @jaheba
Simplify setup.py (#2525) @jaheba
Test: Only check relevant require-packages.txt for test run. (#2595) @Jahaba
Fix version in requirements to comply with stricter setuptools. (#2604) @lostella