- 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
am 23 Apr. 2024
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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