Defined
- Supervised learning algorithm that forecasts one-dimensional (scalar) time series using recurrent neural networks (RNN)
- Trains a single model jointly over all of the similar time series in your dataset
- Use your trained model to create forecasts for new time series that are similar to the ones on which it was trained
- Training input is one or more target time series that have been generated by the same process or similar processes
- Uses an approximation of the process(es) and predicts how the target time series evolves
Use Cases
- Forecasting global food demand
- Predicting global temperature
- Predicting fast-food queues
- Predicting rainfall
- Predicting stock prices
- Options pricing
- Disease progression
- Supply chain management
SageMaker Algorithms
DeepAR
- Uses a training dataset and an optional test dataset
- Use a trained model to predict forecasts for the future of the time series in the training set, or for other time series
- Automatically derives time series based on the frequency of the target series
- For example: a day series generates
day-of-week
,day-of-month
,day-of-year
- For example: a day series generates
- Can predict “what if ?” scenarios, such as, what if I change/broaden the distribution of my product ?
- Builds a single model for all time-series and tries to identify similarities across them
- Important Hyperparameters
-
context_length
: the number of time-points that the mode gets to see before making the prediction -
epochs
: the maximum number of passes over the training data -
prediction_length
: the number of time-steps that the model is trained to predict, also called the forecast horizon -
time_freq
: the granularity of the time series in the dataset
-
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