Darts provides a unified interface for time series forecasting and anomaly detection, spanning traditional statistical models like ARIMA to deep learning architectures. It supports training on large multivariate time series datasets and includes utilities for backtesting, model ensembling, and incorporating external covariates. The library integrates with popular ML frameworks and is designed to be accessible via fit/predict methods similar to scikit-learn.