Time series are everywhere in science and business, and the ability to forecast them accurately and efficiently can provide decisive advantages. For much of its history, time series forecasting has mostly been relying on “classical” statistical methods such as ARIMA. These methods work very well in many cases, but they are not appropriate for capturing patterns in large quantities of data. Very recently, deep learning techniques have been proposed as a way to build very advanced and accurate models from large quantities of time series data. Darts by Unit8 is a Python open source library that provides ready-to-use implementations of all sorts of forecasting models. It puts emphasis on reducing the experiment cycle duration and improving the ease of using, comparing and combining different models. In this talk, we will present how darts can easily be used to solve real business problems and show the simplicity to switch from classical statistical regression models to deep learning techniques.

– Learn more about darts – https://medium.com/unit8-machine-learning-publication/darts-time-series-made-easy-in-python-5ac2947a8878

– Access to the code – https://github.com/unit8co/darts