A graph of the Dow Jones index to symbolize the SageMaker DeepAR Forecasting algorithm

DeepAR Forecasting Algorithm

The SageMaker DeepAR Forecasting Algorithm forecasts how the target time series will evolve based on past performance. AR, which stands for AutoRegression, is a statistical method that uses observations from previous time steps as input to a regression equation to predict the value at the next time step. The forecast is a one dimensional time series.

The input data can contain more than one set of time series data that are related and created by similar methods. DeepAR works best when there are at least 300 observations per dataset and there are hundreds of related datasets. Otherwise more traditional approaches may produce better results. The SageMaker DeepAR algorithm was launched in January 2018

The DeepAR Forecasting Algorithm uses Recurrent Neural Networks to outperform regular algorithms by using multiple time series, rather than a single one.

Attributes

Problem attributeDescription
Data types and formatTabular, numeric time series data
Learning paradigm or domainSupervised Learning
Problem typeTime-series forecasting
Use case examplesBased on historical data for a behavior, predict future behavior

Training

DeepAR Forecasting Algorithm training data is presented in JSON Lines format which can be compressed in gzip or parquet.

  • json
  • json.gz
  • parquet

Model artifacts and inference

DescriptionArtifacts
Learning paradigmSupervised Learning
Request formatJSON
ResultJSON

Processing environment

Training can be done on CPU or GPU. GPU should be used when the training data is large enough to benefit from the additional processing power.

Inference uses CPU instances only.

Video

Amazon SageMaker’s Built-in Algorithm Webinar Series: DeepAR Forecasting

A 53.40 minutes video from AWS.

Credits

Dow Jones graph Markus Spiske on Unsplash

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