Introduction
Darts is an open-source Python library built for easy, end-to-end forecasting, offering a rich set of models, utilities, and APIs to streamline everything from baseline forecasting to deep learning–based temporal modeling. Darts supports a wide range of forecasting methods—from ARIMA and exponential smoothing to LSTMs, Transformer-based models, and probabilistic methods—and provides a seamless interface for training, backtesting, ensembling, and inference. Darts can be leveraged to build scalable, interpretable, and modular forecasting pipelines that support business intelligence, capacity planning, anomaly detection, and ML observability.
Key Benefits of Using Darts include:
Unified Forecasting Interface: Provides a consistent and intuitive API across all models, making it easy to swap between statistical, ML, and deep learning approaches.
Broad Model Support: Includes dozens of prebuilt models—such as Prophet, NBEATS, RNNs, Transformer, TCN, Theta, and regression-based pipelines.
Probabilistic and Quantile Forecasting: Enables modeling of uncertainty with tools like Monte Carlo dropout and quantile regression for better risk estimation.
Backtesting and Evaluation: Built-in utilities for rolling and expanding window backtests, performance metrics, and error analysis to validate models over time.
Data Preprocessing & Feature Engineering: Offers tools for handling missing values, normalization, lag creation, time covariates, and multivariate conditioning.
Use Cases
Darts is used for:
User activity and engagement forecasting: Predicting session volume, feature usage trends, and retention patterns across time windows.
ML model behavior monitoring: Forecasting latency, token usage, and API throughput to flag anomalies and capacity limits proactively.
Business KPIs and experimentation tracking: Modeling trends in conversion rates, experiment outcomes, and seasonal performance.
Infrastructure and operations planning: Forecasting cluster usage, memory footprints, and pipeline runtimes to inform autoscaling and resource provisioning.
Darts integrates easily with Python-based orchestration tools like Airflow, Dagster, and Prefect, and can be complemented by observability tools like Grafana, Evidently, and NannyML for robust temporal monitoring and alerting. Darts empowers its teams to build reliable, interpretable, and high-performing time series models—driving better forecasting, smarter anomaly detection, and more proactive decision-making across the platform.