[MLS-C01] [Algorithms] Forecasting Algorithms

Posted by Oscaner on August 7, 2022


  • 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

  1. Forecasting global food demand
  2. Predicting global temperature
  3. Predicting fast-food queues
  4. Predicting rainfall
  5. Predicting stock prices
  6. Options pricing
  7. Disease progression
  8. Supply chain management

SageMaker Algorithms


  • 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
  • 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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