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Probabilistic time series modeling in Python
| Date | Stars |
|---|---|
| 2026-07-24 | 5222 |
| 2026-07-25 | 5222 |
| 2026-07-28 | 5222 |
| 2026-07-30 | 5222 |
| 2026-08-06 | 5222 |
Today
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Momentum
15.0
growth rate 0.00%/day
<img class="hide-on-website" height="100px" src="https://ts.gluon.ai/dev/_static/gluonts.svg">
# GluonTS - Probabilistic Time Series Modeling in Python
[](https://pypi.org/project/gluonts/)
[](./LICENSE)
[](https://ts.gluon.ai/)
[](https://ts.gluon.ai/dev/)
[](https://pepy.tech/projects/gluonts)
**📢 BREAKING NEWS**: We released **Chronos**, a suite of pretrained models for zero-shot time series forecasting. Chronos can generate accurate probabilistic predictions for new time series not seen during training. Check it out [here](https://github.com/amazon-science/chronos-forecasting)!
GluonTS is a Python package for probabilistic time series modeling, focusing on deep learning based models,
based on [PyTorch](https://pytorch.org).
## Installation
GluonTS requires Python 3.10 to 3.14. We recommend using
[uv](https://github.com/astral-sh/uv) for managing environments:
```bash
# install with support for torch models
uv pip install "gluonts[torch]"
```
For development:
```bash
# clone the repository
git clone https://github.com/awslabs/gluonts.git
cd gluonts
# install with all development dependencies
uv sync --all-extras
```
You can also install via `pip`:
```bash
pip install "gluonts[torch]"
```
See the [documentation](https://ts.gluon.ai/stable/getting_started/install.html)
for more info on how GluonTS can be installed.
## Simple Example
To illustrate how to use GluonTS, we train a DeepAR-model and make predictions
using the airpassengers dataset. The dataset consists of a single time
series of monthly passenger numbers between 1949 and 1960. We train the model
on the first nine years and make predictions for the remaining three years.
```py
import pandas as pd
import matplotlib.pyplot as plt
from gluonts.dataset.pandas import PandasDataset
from gluonts.dataset.split import split
from gluonts.torch import DeepAREstimator
# Load data from a CSV file into a PandasDataset
df = pd.read_csv(
"https://raw.githubusercontent.com/AileenNielsen/"
"TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv",
index_col=0,
parse_dates=True,
)
dataset = PandasDataset(df, target="#Passengers")
# Split the data for training and testing
training_data, test_gen = split(dataset, offset=-36)
test_data = test_gen.generate_instances(prediction_length=12, windows=3)
# Train the model and make predictions
model = DeepAREstimator(
prediction_length=12, freq="M", trainer_kwargs={"max_epochs": 5}
).train(training_data)
forecasts = list(model.predict(test_data.input))
# Plot predictions
plt.plot(df["1954":], color="black")
for forecast in forecasts:
forecast.plot()
plt.legend(["True values"], loc="upper left", fontsize="xx-large")
plt.show()
```
![[train-test]](https://ts.gluon.ai/static/README/forecasts.png)
Note, the forecasts are displayed in terms of a probability distribution and
the shaded areas represent the 50% and 90% prediction intervals.
## Contributing
If you wish to contribute to the project, please refer to our
[contribution guidelines](https://github.com/awslabs/gluonts/tree/dev/CONTRIBUTING.md).
## Citing
If you use GluonTS in a scientific publication, we encourage you to add the following references to the related papers,
in addition to any model-specific references that are relevant for your work:
```bibtex
@article{gluonts_jmlr,
author = {Alexander Alexandrov and Konstantinos Benidis and Michael Bohlke-Schneider
and Valentin Flunkert and Jan Gasthaus and Tim Januschowski and Danielle C. Maddix
and Syama Rangapuram and David Salinas and Jasper ScExcerpt of 5,915 characters
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Lorenzo Stella · AWS @aws @awslabs · Germany
389
Kashif Rasul · Germany
56
43
Valentin Flunkert · Amazon
39
Abdul Fatir · Amazon Web Services · Germany
34
33
Oleksandr Shchur · Germany
26
Konstantinos Benidis · Amazon AI · Germany
25
25
24
Jan Gasthaus · Meta · Germany
21
18
17
16
Caner Turkmen · @keystone-ai · Germany
12
12
10
Eddie
10
10
Oliver Borchert · @Quantco · Germany
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3863c49175065ed8, topic:deep-learning, topic:pytorch