This talk will give an introduction to Darts (https://github.com/unit8co/darts), an open-source library for time series processing and forecasting. Darts provides a wide variety of models and tools under a unified and user-friendly API. We will give a high level introduction to both time series forecasting and the main features of Darts.
Time series are everywhere in science and business, and the ability to forecast them accurately and efficiently can provide decisive advantages. Darts is an open-source Python library, which provides a wide variety of forecasting models and tools under a single and user-friendly API. It puts emphasis on reducing the experiment cycle duration and improving the ease of using, comparing and combining different models; from ARIMA to deep learning models.
This talk will give a tour of Darts and some of its main features, such as: quick creation and comparison of forecasting models, backtesting, ML-based models applied to time series forecasting, training forecasting models on multiple time series, producing probabilistic forecasts and integrating external data. We will go over a few toy examples, and see how to address them in a few lines of code.
Goals of the talk: