Best DARTS Alternatives ranked by AI · updated Aug 2026

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Darts is an open-source Python library for forecasting and modeling univariate and multivariate time series. It supports classical statistical models, machine learning, deep learning, backtesting, covariates, and probabilistic forecasts through a unified API.

Developer: Unit8 Price: Free 🎯 unit8co.github.io/darts

🔓 Open Source 🤖 Machine Learning Forecasting Python time series

Top 6 DARTS alternatives

StatsForecast

Nixtla

💡 Pick it for fast, scalable statistical forecasting across many time series.

StatsForecast is an open-source Python library for statistical and econometric time-series forecasting. It targets analysts and engineers who need fast implementations of...

Pros

Typically faster and more memory-efficient than Darts for large collections of series
Strong automated statistical baselines with minimal configuration
Better suited to panel forecasting than many general-purpose libraries

Cons

Fewer deep learning models than Darts
Less flexible for custom neural architectures and complex covariates
More limited unified support for probabilistic deep forecasting

Free

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NeuralForecast

Nixtla

💡 Choose it when neural forecasting performance and scalable PyTorch models matter most.

NeuralForecast is an open-source Python library of PyTorch-based neural forecasting models. It is designed for practitioners who need modern architectures such as...

Pros

More focused neural forecasting model catalog than Darts
Efficient implementations for large datasets and multiple series
Includes strong recent architectures such as N-HiTS and PatchTST

Cons

Narrower coverage of classical models than Darts
Requires more GPU and training expertise than statistical libraries
Less convenient for mixed model experimentation in one API

Free

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GluonTS

Amazon Web Services

💡 Pick it for probabilistic deep forecasting and research-oriented model development.

GluonTS is an open-source Python toolkit for probabilistic time-series modeling, forecasting, and evaluation. It is intended for developers and researchers who want...

Pros

Avoids managed-service fees and vendor lock-in
Provides probabilistic forecasting models and evaluation utilities
Allows full control over data, training, architectures, and deployment

Cons

Requires substantially more engineering than Amazon Forecast
You must operate training, serving, scaling, and monitoring
Fewer turnkey business workflows and integrations than cloud platforms

Free, open source

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PyTorch Forecasting

PyTorch Forecasting contributors

💡 Choose it for customizable PyTorch models with rich covariate and Lightning integration.

PyTorch Forecasting is an open-source library for training neural time-series models with PyTorch Lightning. It is intended for data scientists who want...

Pros

Excellent integration with the PyTorch and Lightning ecosystem
Strong support for covariates, categorical variables, and multi-horizon forecasting
Temporal Fusion Transformer offers useful interpretability features

Cons

Less suitable than Darts for classical statistical forecasting
More implementation complexity for small or simple projects
Version compatibility across PyTorch and Lightning can require attention

Free

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sktime

sktime community

💡 Pick it for scikit-learn-style pipelines and a broader time-series analysis toolkit.

sktime is an open-source Python framework for time-series analysis, forecasting, and related machine-learning tasks. It provides a scikit-learn-style interface, composable pipelines, reduction...

Pros

Broader time-series task coverage than Darts, including classification and transformation
Familiar estimator, pipeline, and model-selection patterns for scikit-learn users
Strong composability and benchmarking capabilities