Darts: User-Friendly Modern Machine Learning for Time Series

Abstract

We present Darts, a Python machine learning library for time series, with a focus on forecasting. Darts offers a variety of models, from classics such as ARIMA to state-of-the-art deep neural networks. The emphasis of the library is on offering modern machine learning functionalities, such as supporting multidimensional series, meta-learning on multiple series, training on large datasets, incorporating external data, ensembling models, and providing a rich support for probabilistic forecasting. At the same time, great care goes into the API design to make it user-friendly and easy to use. For instance, all models can be used using fit()/predict(), similar to scikit-learn.

Darts Github repository: https://github.com/unit8co/darts

References in corpus (1)

• Merlion: A Machine Learning Library for Time Series

Cited by in corpus (5)

• Transfer Learning in Transformer-Based Demand Forecasting For Home Energy Management System
• Continually learning out-of-distribution spatiotemporal data for robust energy forecasting
• Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning
• Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting
• A House United Within Itself: SLO-Awareness for On-Premises Containerized ML Inference Clusters via Faro