darts

library0.44.1pypypiunverified

Darts is a Python library for user-friendly forecasting and anomaly detection on time series. It offers a unified API for a wide range of models, from classical statistical methods like ARIMA to advanced deep learning architectures such as LSTM and Transformers. The library also includes tools for model evaluation, backtesting, handling multiple time series, and incorporating external covariates. It is actively maintained with frequent minor releases, typically on a monthly basis. [1, 2, 4, 9, 11, 13, 16, 22]

ai-ml data

pip install darts

github ↗package ↗homepage ↗docs ↗

INSTALL

IMPORT

SIG · DARTS

darts

ai-mlpythonv0.44.1

Install

29.1s avg

Import

1251ms

Disk

739MB

Pass rate

4/ 10

Env Coverage4 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.44.1 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✕ build_error

✓ 31.08s

py 3.11

✕ build_error

✓ 30.4s

py 3.12

✕ build_error

✓ 27.15s

py 3.13

✕ build_error

✓ 27.6s

py 3.9

✕ build_error

✕ timeout

739MB installed

● package 739MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

TimeSeries

✓ from darts import TimeSeries

ARIMA

✓ from darts.models import ARIMA

✗ from darts.models import AutoARIMA

AutoARIMA is part of pmdarima integration, ARIMA is a separate Darts model.

ExponentialSmoothing

✓ from darts.models import ExponentialSmoothing

This quickstart demonstrates how to create a `TimeSeries` object from a Pandas DataFrame, split it into training and validation sets, fit an `ExponentialSmoothing` model, and generate a forecast. The example then plots the actual series against the predicted values. [4, 8, 11, 12, 13]

import pandas as pd from darts import TimeSeries from darts.models import ExponentialSmoothing import matplotlib.pyplot as plt # Create a sample DataFrame (replace with your data) df = pd.DataFrame({ 'Month': pd.to_datetime(pd.date_range(start='2000-01-01', periods=120, freq='MS')), '#Passengers': [100 + i + (i**1.2) * 0.5 for i in range(120)] # Example data }) # Create a TimeSeries object series = TimeSeries.from_dataframe(df, 'Month', '#Passengers') # Split data into training and validation sets train, val = series[:-12], series[-12:] # Initialize and fit a model model = ExponentialSmoothing() model.fit(train) # Make a prediction prediction = model.predict(len(val)) # Plot the results series.plot(label='actual') prediction.plot(label='forecast') plt.title('Darts Quickstart Forecast') plt.show()

Debug

Known issues

breakingDarts version 0.41.0 and later removed support for Python 3.9. The new minimum Python version is 3.10. [9]

fix

Upgrade your Python environment to 3.10 or higher.

affects: >=0.41.0

breakingAs of Darts version 0.41.0, the PyPI package name changed from `u8darts` to `darts`. The `u8darts` package is no longer maintained. [1, 9]

fix

Use `pip install darts` for installation and `from darts import ...` for imports. Do not use `u8darts`.

affects: >=0.41.0

gotchaTraining or forecasting with `TimeSeries` objects containing `NaN` values will often lead to `NaN`s in forecasts or errors. [20]

fix

Ensure your input `TimeSeries` objects (targets and covariates) are free of NaNs. This often involves properly handling missing dates and ensuring the `freq` argument is correctly set when creating a `TimeSeries` from a DataFrame. [20]

affects: All

gotchaInstalling Darts with all optional dependencies (`pip install "darts[all]"` or `pip install "darts[torch]"`) can be complex due to non-Python dependencies for libraries like Prophet and PyTorch. [1, 3, 5, 17]

fix

Consider using a Conda environment for easier dependency management (`conda install -c conda-forge -c pytorch u8darts-all`) or install specific optional dependencies separately after the core Darts package, following their respective installation guides. [1, 3]

affects: All

Upgrade

Version history

0.44.1latest on PyPI · released May 5, 2026

Audit

Dependencies

pythonrequiredRequires Python 3.10 or higher.

torchoptionalRequired for neural network models (e.g., LSTM, TCN, N-BEATS). Install with `pip install "darts[torch]"`.

prophetoptionalRequired for the Prophet model. Install with `pip install "darts[notorch]"` (includes other ML models too) or `pip install "darts[all]"`.

pmdarimaoptionalRequired for AutoARIMA model. Included in `pip install "darts[notorch]"` or `pip install "darts[all]"`.

pytorch-lightningrequiredUnderpins deep learning models. Minimum version >=2.0.0 for Darts >=0.41.0.

Agent activity

34 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/unit8co/darts ↗

packagehttps://pypi.org/project/darts/ ↗

homepagehttps://unit8co.github.io/darts/ ↗

docshttps://unit8co.github.io/darts/ ↗