Deep AR Forecasting¶
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future.
In many applications, however, you have many similar time series across a set of cross-sectional units. For example, you might have time series groupings for demand for different products, server loads, and requests for webpages. For this type of application, you can benefit from training a single model jointly over all of the time series. DeepAR takes this approach. When your dataset contains hundreds of related time series, DeepAR outperforms the standard ARIMA and ETS methods. You can also use the trained model to generate forecasts for new time series that are similar to the ones it has been trained on.
The training input for the DeepAR algorithm is one or, preferably, more target time series that have been generated by the same process or similar processes. Based on this input dataset, the algorithm trains a model that learns an approximation of this process/processes and uses it to predict how the target time series evolves. Each target time series can be optionally associated with a vector of static (time-independent) categorical features provided by the cat field and a vector of dynamic (time-dependent) time series provided by the dynamic_feat field. SageMaker trains the DeepAR model by randomly sampling training examples from each target time series in the training dataset. Each training example consists of a pair of adjacent context and prediction windows with fixed predefined lengths. To control how far in the past the network can see, use the context_length hyperparameter. To control how far in the future predictions can be made, use the prediction_length hyperparameter. For more information, see How the DeepAR Algorithm Works.
For a sample notebook that shows how to prepare a time series dataset for training the SageMaker DeepAR algorithm and how to deploy the trained model for performing inferences, see Time series forecasting with DeepAR - Synthetic data as well as DeepAR demo on electricity dataset, which illustrates the advanced features of DeepAR on a real world dataset. For instructions on creating and accessing Jupyter notebook instances that you can use to run the example in SageMaker, see Use Amazon SageMaker Notebook Instances. After creating and opening a notebook instance, choose the SageMaker Examples tab to see a list of all of the SageMaker examples. To open a notebook, choose its Use tab, and choose Create copy.