A Python library for user-friendly forecasting and anomaly detection on time series, from ARIMA to deep neural networks.
Darts is a Python library that provides a user-friendly interface for time series forecasting and anomaly detection. It unifies a wide variety of models, from traditional statistical methods like ARIMA to contemporary deep neural networks, under a consistent API similar to scikit-learn. The library solves the problem of fragmented tooling by offering a single, cohesive toolkit for building, evaluating, and deploying time series models.
Data scientists, machine learning engineers, and analysts working on time series problems who need a flexible, all-in-one library for forecasting and anomaly detection. It's particularly valuable for teams requiring both classical and cutting-edge models with support for multivariate data and probabilistic predictions.
Developers choose Darts for its extensive model collection, consistent API that reduces learning curves, and strong support for advanced features like global training on multiple series and probabilistic forecasting. Its integration with popular libraries like PyTorch and scikit-learn, combined with built-in anomaly detection capabilities, makes it a versatile and powerful alternative to assembling disparate specialized tools.
A python library for user-friendly forecasting and anomaly detection on time series.
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All models use consistent fit() and predict() methods, enabling easy model swapping and reducing the learning curve, as emphasized in the README's comparison to scikit-learn.
Includes over 50 models from classics like ARIMA to deep learning (N-BEATS, TCN) and wrappers for StatsForecast and NeuralForecast, providing a one-stop shop for diverse forecasting needs.
Handles multiple time-varying dimensions and offers probabilistic forecasting with confidence intervals, crucial for risk-aware decisions in complex datasets.
The darts.ad module simplifies anomaly detection with scorers, detectors, and PyOD integration, allowing seamless combination with forecasting models for end-to-end analysis.
Machine learning models can be trained on multiple time series datasets, scaling to large volumes and improving generalization, as highlighted in the features list.
Relies on PyTorch, scikit-learn, and other large libraries, making installation and deployment cumbersome, especially when minimizing package size or managing dependencies is critical.
Optimized for batch processing and backtesting, with no native support for streaming data or online learning, which can be a drawback for dynamic, real-time applications.
For straightforward tasks with classical models, using Darts may introduce unnecessary abstraction overhead compared to lightweight alternatives like statsmodels or Prophet directly.
While it abstracts many details, deep model customization or integration with non-standard pipelines often requires delving into underlying libraries, adding complexity for advanced use cases.