Faster time series forecasting at scale using Ray and Anyscale

Forecasting is an important part of running any business. The “embarrassingly parallel” pattern of distributed computing works with time series algorithms such as ARIMA, Prophet, and Neural Prophet. For global deep learning algorithms, both data and model parallelism become simple and easy with Ray.

In this webinar, we will demo both patterns. For the deep learning algorithm, we will show how to take Google’s Temporal Fusion Transformer, available in PyTorch Forecasting, and turn training and inference into distributed applications using Ray. Ray code runs in parallel across local laptop cores. The exact same code can run in parallel across any cluster in any cloud too.