[MLS-C01] [Algorithms] Forecasting Algorithms

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

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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