chronos-forecasting
Chronos: Pretrained models for time series forecasting
Decision gist · record as of 2026-08-14
Yes. Chronos is actively maintained, has no known vulnerabilities, and offers a straightforward interface to state-of-the-art pretrained models. Install it if you need time series forecasting without the overhead of training your own model. The low install friction and permissive license make it a practical choice for prototyping and production use. Consider it essential if you're forecasting on new domains where labeled training data is scarce.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10.
- torch and transformers must be installed; GPU recommended for inference speed but CPU inference is supported via device_map.
- Low install friction with a pure-Python wheel.
License · maintenance · safety
permissive license (permissive) — Apache License 2.0 permits commercial and derivative use with minimal restrictions. You must include a copy of the license and note any modifications, but there are no copyleft obligations or patent claims that would restrict downstream use.
last release 2026-07-02 (43 days) · last repo commit 2026-08-14 · 5,708 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 666,996 downloads/mo, #5,423 on PyPI
Alternatives
Verify before relying
pip install chronos-forecasting
from chronos import Chronos2Pipeline
import pandas as pd
pipeline = Chronos2Pipeline.from_pretrained("amazon/chronos-2", device_map="cuda")
context_df = pd.read_parquet("train.parquet")
pred_df = pipeline.predict_df(context_df, prediction_length=24, quantile_levels=[0.1, 0.5, 0.9], id_column="id", timestamp_column="timestamp", target="target")- Whether pretrained model weights are automatically downloaded on first use or require manual setup.
- Memory and disk requirements for different model sizes (8M to 710M parameters).
- Inference latency and throughput benchmarks on CPU vs. GPU hardware.
- Whether the package supports batch inference across multiple time series simultaneously.
What it is and what it does
Chronos is a library that wraps pretrained language-model-based time series forecasting models. It transforms historical time series data into token sequences, uses transformer architectures to learn patterns, and generates probabilistic forecasts by sampling multiple future trajectories or computing quantile predictions. The library includes three model families: Chronos-2 (latest, 120M parameters, best zero-shot performance), Chronos-Bolt (patch-based, up to 250 times faster and 20 times more memory-efficient), and the original Chronos (T5-based, ranging from 8M to 710M parameters).
You use it by loading a pretrained model via HuggingFace, passing historical time series data as a pandas DataFrame with optional covariate columns, and calling predict_df to get quantile forecasts for future steps. The library handles the tokenization, model inference, and output formatting automatically. It's designed for zero-shot forecasting—applying a model trained on diverse time series to new domains without fine-tuning—and supports both univariate (single target column) and multivariate (multiple targets plus exogenous features) scenarios.
Use it for
- Forecast electricity prices or demand using historical data and optional future weather or market covariates.
- Generate probabilistic predictions for stock prices or financial metrics with quantile-based confidence intervals.
- Predict website traffic, server load, or system metrics for capacity planning without retraining.
- Perform rapid prototyping of time series predictions on new datasets without labeled training data.
- Deploy production forecasting endpoints on AWS SageMaker or AutoGluon-Cloud for real-time or batch inference.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Chronos is actively maintained, has no known vulnerabilities, and offers a straightforward interface to state-of-the-art pretrained models. Install it if you need time series forecasting without the overhead of training your own model. The low install friction and permissive license make it a practical choice for prototyping and production use. Consider it essential if you're forecasting on new domains where labeled training data is scarce.
Install
chronos-forecasting on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance with recent releases (last commit 2026-08-14, 43 days since latest version). Depends on torch, transformers, and accelerate—standard ML stack dependencies that may require significant disk space but are widely available.
Requires Python >=3.10. torch and transformers must be installed; GPU recommended for inference speed but CPU inference is supported via device_map.
License in practice
Apache License 2.0 permits commercial and derivative use with minimal restrictions. You must include a copy of the license and note any modifications, but there are no copyleft obligations or patent claims that would restrict downstream use.
Quickstart
pip install chronos-forecasting
from chronos import Chronos2Pipeline
import pandas as pd
pipeline = Chronos2Pipeline.from_pretrained("amazon/chronos-2", device_map="cuda")
context_df = pd.read_parquet("train.parquet")
pred_df = pipeline.predict_df(context_df, prediction_length=24, quantile_levels=[0.1, 0.5, 0.9], id_column="id", timestamp_column="timestamp", target="target")Verify before relying
- Whether pretrained model weights are automatically downloaded on first use or require manual setup.
- Memory and disk requirements for different model sizes (8M to 710M parameters).
- Inference latency and throughput benchmarks on CPU vs. GPU hardware.
- Whether the package supports batch inference across multiple time series simultaneously.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagesaccelerateeinopsnumpypandastorchtransformers |
| Maintenance | Actively maintained 43 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 666,996 / month, #5,423 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: chronos_forecasting-2.3.1-py3-none-any.whl
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