autogluon.timeseries
Fast and Accurate ML in 3 Lines of Code
Decision gist · record as of 2026-08-14
Yes, with conditions. Install if you need time series forecasting with minimal manual model selection and have the disk space and compute for PyTorch and 27 dependencies. The active maintenance, permissive license, low install friction, and zero known vulnerabilities make it a solid choice. Skip if you need lightweight, dependency-minimal forecasting or are constrained to Python <3.10.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10–3.13; PyTorch and other ML dependencies will download on first install, which may require several gigabytes of disk space.
- Low install friction with a pure-Python wheel.
- Active maintenance with a recent release (8 days old) and strong repository signals (10596 stars).
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows use in commercial and private projects with minimal restrictions, requiring only license and copyright notice retention.
last release 2026-08-06 (8 days) · last repo commit 2026-08-14 · 10,596 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 347,272 downloads/mo, #7,349 on PyPI
Alternatives
Verify before relying
pip install autogluon.timeseries
from autogluon.timeseries import TimeSeriesPredictor
predictor = TimeSeriesPredictor(label="target").fit(train_data, presets="best")
predictions = predictor.predict(test_data)- Whether the package includes GPU acceleration support or if that requires separate installation steps.
- Specific performance benchmarks or accuracy claims for different forecasting scenarios.
- Memory and compute requirements for typical time series datasets.
What it is and what it does
AutoGluon TimeSeries is an automated machine learning library that simplifies time series forecasting by automatically selecting, training, and ensembling models from deep learning, statistical, and foundation model approaches. It wraps complex model selection and hyperparameter tuning into a few lines of code, targeting developers who want strong forecasting performance without manual model engineering.
The package depends on a large ecosystem: PyTorch and Lightning for neural networks, Hugging Face transformers and PEFT for foundation models, GluonTS and specialized forecasting libraries (statsforecast, mlforecast, chronos-forecasting) for statistical and hybrid approaches, and AutoGluon's own tabular and feature modules for preprocessing and ensemble logic. It is actively maintained, supports Python 3.10–3.13, and runs on Linux, macOS, and Windows.
Use it for
- Build a demand forecasting model for supply chain without manually tuning ARIMA, Prophet, or neural network hyperparameters.
- Forecast financial time series (stock prices, trading volumes) by combining statistical and deep learning models automatically.
- Generate probabilistic forecasts with uncertainty intervals for risk assessment in healthcare or operations planning.
- Rapidly prototype multiple forecasting approaches on new datasets to identify which model class performs best.
- Deploy production forecasting pipelines that adapt to new data by retraining with minimal code changes.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
Install if you need time series forecasting with minimal manual model selection and have the disk space and compute for PyTorch and 27 dependencies. The active maintenance, permissive license, low install friction, and zero known vulnerabilities make it a solid choice. Skip if you need lightweight, dependency-minimal forecasting or are constrained to Python <3.10.
Install
autogluon-timeseries on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance with a recent release (8 days old) and strong repository signals (10596 stars). Requires 27 runtime dependencies including PyTorch, Lightning, Hugging Face transformers, and specialized forecasting libraries, which may take time to resolve but are well-established packages.
Requires Python 3.10–3.13; PyTorch and other ML dependencies will download on first install, which may require several gigabytes of disk space.
License in practice
Apache-2.0 permissive license allows use in commercial and private projects with minimal restrictions, requiring only license and copyright notice retention.
Quickstart
pip install autogluon.timeseries
from autogluon.timeseries import TimeSeriesPredictor
predictor = TimeSeriesPredictor(label="target").fit(train_data, presets="best")
predictions = predictor.predict(test_data)Verify before relying
- Whether the package includes GPU acceleration support or if that requires separate installation steps.
- Specific performance benchmarks or accuracy claims for different forecasting scenarios.
- Memory and compute requirements for typical time series datasets.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <3.14,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 27 packagesjoblibnumpyscipypandastorchlightningtransformersacceleratehuggingface_hubsafetensorsgluontsnetworkxstatsforecastmlforecastutilsforecastcoreforecastfuguetqdmorjsoneinopschronos-forecastingpefttensorboardautogluon.coreautogluon.commonautogluon.featuresautogluon.tabular |
| Maintenance | Actively maintained 8 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 347,272 / month, #7,349 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: Customer ServiceIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Financial and Insurance IndustryIntended Audience :: Healthcare IndustryIntended Audience :: Science/ResearchIntended Audience :: Telecommunications IndustryOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Image RecognitionTopic :: Scientific/Engineering :: Information AnalysisTopic :: Software Development |
Evidence: autogluon_timeseries-1.6.1-py3-none-any.whl
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See also autogluon · autogluon.core · chronos-forecasting · autogluon.multimodal · autogluon.tabular · gluonts · autogluon.common · autogluon.features · pyglove · autogluon.text