Answers.Body: <p>I have have been part of a project where we implemented Amazon Forecast service in production. As per my practical experience, we need not worry about stationarity while applying DeepAR.</p>
  <p>As per my understanding as in ARIMA the prediction function is a function around the time series moving average, but in DeepAR we are more relying on the backtest window and leg-horizon length.</p>
  <p>DeepAR uses recurrent neural Networks (RNN) and gated recurrent unit (GRU) without any assumption on their probability distribution to learn the sequence for prediction.</p>
  <p>For a detailed explanation, you can refer. <a href="https://aws.amazon.com/blogs/machine-learning/forecasting-time-series-with-dynamic-deep-learning-on-aws/" rel="nofollow noreferrer">https://aws.amazon.com/blogs/machine-learning/forecasting-time-series-with-dynamic-deep-learning-on-aws/</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-10-29T14:25:38.743Z
Answers.Text: in the official video of aws, it says that data MUST be stationary. https://youtu.be/g8UYGh0tlK0?t=1406
Answers.UserDisplayName: Soroush
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-07-30T13:36:20.130Z
Answers.LastActivityDate: 2020-07-30T13:36:20.130Z
Answers.UserDisplayName: Sanmati Jain
Body: <p>As what the title said. Does not deepAR require the time series being stationary?</p>
  
Tags: <deep-neural-networks><time-series>
Title: Why does not the deepAR model of Amazon require the time series being stationary, as opposed to ARMA model?
UserDisplayName: abc
ThreadText: # Why does not the deepAR model of Amazon require the time series being stationary, as opposed to ARMA model?
  
  (Asked by: username_0 on 2020-07-28)
  
  As what the title said. Does not deepAR require the time series being stationary?
  
  **Tags:** deep-neural-networks, time-series
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2020-07-30. Score: 0
  
  I have have been part of a project where we implemented Amazon Forecast service in production. As per my practical experience, we need not worry about stationarity while applying DeepAR.
  
  As per my understanding as in ARIMA the prediction function is a function around the time series moving average, but in DeepAR we are more relying on the backtest window and leg-horizon length.
  
  DeepAR uses recurrent neural Networks (RNN) and gated recurrent unit (GRU) without any assumption on their probability distribution to learn the sequence for prediction.
  
  For a detailed explanation, you can refer. <https://aws.amazon.com/blogs/machine-learning/forecasting-time-series-with-dynamic-deep-learning-on-aws/>
  
  > **Comments:**
  > **username_2**: in the official video of aws, it says that data MUST be stationary. https://youtu.be/g8UYGh0tlK0?t=1406  
  
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