Hi all,
I attempt to use the long short-term memory (LSTM) of a deep learning method to generate the precipitation ensemble of 20 CMIP6 model simulations for SSP scenarios. Dear all, could anybody provide some memo or specification about LSTM in ensemble use? Anyway, thank you!
Dear Sir Mingxing Li :
Ensemble modeling is a process in which multiple diverse models are created to predict an outcome, either by using many different modeling algorithms or different training data sets. The ensemble model then aggregates the prediction of each base model and results in one final prediction for the unseen data. The specification about LSTM in ensemble usage is discussed in the following links:
- https://machinelearningmastery.com/ensemble-methods-for-deep-learning-neural-networks/
- https://machinelearningmastery.com/stacking-ensemble-for-deep-learning-neural-networks/
Regards;
Ehtisham
Hi Ehtisham Lodhi,
Thank you sincerely for your response and useful links.
Cheers,
Mingxing
It seems that LSTM-like models might not good at learning the long-term trends in time series and thus the predicted time series have low accuracy in long-term trends, even they show good variations (e.g., high correlation coefficients).
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