- Use Dropout in the LSTM layers not just during training but also during prediction for randomness.
- Make multiple predictions for the same input data with dropout enabled.
- The multiple distributions for each time step form an empirical distribution that can be used as Predictive Density.
- The variance in these predictions can be used to estimate the Prediction Interval.
How to calculate prediction intervals with LSTM's deterministic prediction?
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How to calculate prediction interval/ predictive density with LSTM time series point forecast data?
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Aneela
2024-4-23
Hi Israt Fatema,
Monte Carlo Dropout technique can be used to estimate prediction intervals and predictive density for LSTM.
Refer to the following MathWorks documentation for more information on Dropout layer:. https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.dropoutlayer.html
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