Scalable Time Series Forecasting with DeepAR.

This blog is the first of a two-part series that will provide a detailed overview of the state-of-art deep learning model DeepAR and a comparison of it to the state-of-art classical method Fb-Prophet. Whereas the second article explores a use case based end to end implementation of DeepAR algorithm in AWS sagemaker. DeepAR is a forecasting methodology based on AR RNN that learns a global model instead of fitting separate models for each time series like in other classical models. It learns from the historical data of all time-series in the dataset and produces accurate probabilistic forecasts. The technique was developed by Amazon and stands out for its ability to "scale" using multiple covariates.