darts
A python library for easy manipulation and forecasting of time series.
Decision gist · record as of 2026-08-14
Yes. Darts is actively maintained, widely used (top 15000 on PyPI), has no known vulnerabilities, and offers a comprehensive, production-ready toolkit for time series forecasting and anomaly detection. The scikit-learn-style API and support for both classical and deep learning models make it suitable for both prototyping and deployment. The 18 dependencies are standard ML libraries, not exotic or risky.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; numba and llvmlite are runtime dependencies that may require a C compiler on some systems.
- Low friction installation with a pure-Python wheel.
- Active maintenance with recent releases; 18 runtime dependencies including numpy, pandas, scikit-learn, and scipy suggest a mature ML stack.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 (permissive) allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.
last release 2026-07-20 (25 days) · last repo commit 2026-08-06 · 9,493 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 299,040 downloads/mo, #7,864 on PyPI
Alternatives
Verify before relying
pip install darts
from darts import TimeSeries
from darts.models import ExponentialSmoothing
import pandas as pd
df = pd.read_csv("data.csv")
series = TimeSeries.from_dataframe(df, "time_col", "value_col")
train, val = series[:-36], series[-36:]
model = ExponentialSmoothing()
model.fit(train)
prediction = model.predict(len(val))- Whether all 18 runtime dependencies are strictly required for basic forecasting or if some are optional for specific models.
- Performance characteristics when training on large multivariate datasets or with deep learning models.
What it is and what it does
Darts is a time series forecasting and anomaly detection library that unifies classical statistical models (ARIMA, exponential smoothing) and modern deep learning approaches under a single scikit-learn-compatible interface. It handles univariate and multivariate series, supports probabilistic forecasting with confidence intervals, and enables training on multiple series simultaneously.
The library's anomaly detection module wraps forecasting models and integrates PyOD scorers to detect outliers in time series. It also supports external covariates (past-observed and future-known), static metadata, hierarchical reconciliation, and conformal prediction for calibrated uncertainty quantification. The core TimeSeries object abstracts data handling, making it straightforward to preprocess, split, and backtest models.
Use it for
- Forecast demand, sales, or resource usage over time using ARIMA, exponential smoothing, or neural networks.
- Detect anomalies in sensor data, system metrics, or financial time series using k-means or other scorers.
- Train a single model on multiple related time series (e.g., sales across stores) to improve generalization.
- Generate probabilistic forecasts with quantile intervals for risk-aware decision-making.
- Combine predictions from multiple models to reduce forecast error through ensemble methods.
- Incorporate external variables (weather, promotions) as past or future covariates to improve predictions.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Darts is actively maintained, widely used (top 15000 on PyPI), has no known vulnerabilities, and offers a comprehensive, production-ready toolkit for time series forecasting and anomaly detection. The scikit-learn-style API and support for both classical and deep learning models make it suitable for both prototyping and deployment. The 18 dependencies are standard ML libraries, not exotic or risky.
Install
darts on PyPI
Before you install
Low friction installation with a pure-Python wheel. Active maintenance with recent releases; 18 runtime dependencies including numpy, pandas, scikit-learn, and scipy suggest a mature ML stack. Requires Python 3.10+.
Requires Python 3.10 or later; numba and llvmlite are runtime dependencies that may require a C compiler on some systems.
License in practice
Apache-2.0 (permissive) allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.
Quickstart
pip install darts
from darts import TimeSeries
from darts.models import ExponentialSmoothing
import pandas as pd
df = pd.read_csv("data.csv")
series = TimeSeries.from_dataframe(df, "time_col", "value_col")
train, val = series[:-36], series[-36:]
model = ExponentialSmoothing()
model.fit(train)
prediction = model.predict(len(val))Verify before relying
- Whether all 18 runtime dependencies are strictly required for basic forecasting or if some are optional for specific models.
- Performance characteristics when training on large multivariate datasets or with deep learning models.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 18 packagesholidaysjoblibmatplotlibnarwhalsnfoursidnumpypandaspyodrequestsscikit-learnscipyshapstatsmodelstqdmtyping-extensionsxarraynumballvmlite |
| Maintenance | Actively maintained 25 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 299,040 / month, #7,864 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: Implementation :: PyPyTopic :: Scientific/EngineeringTopic :: Software Development |
Evidence: darts-0.46.1-py3-none-any.whl
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