Hi,
I have applied this model to other data sets.
I want to forecast the energy consumption using a history of energy consumption and 23 weather features over 4 years.
The validation loss is increasing, which I assume it is a sign of overfitting. and the training loss does not go under 0.2.
I have tried decreasing the learning rate, adding decay rate, and reducing the lstm layers, but I still have overfitting.
How can I modify my model to prevent overfitting (the increasing trend in the validation loss?
Hi, I have applied this model to other data sets. I want to forecast the energy consumption using a history of energy consumption and 23 weather features over 4 years.
The validation loss is increasing, which I assume it is a sign of overfitting. and the training loss does not go under 0.2. I have tried decreasing the learning rate, adding decay rate, and reducing the lstm layers, but I still have overfitting.
How can I modify my model to prevent overfitting (the increasing trend in the validation loss?
Thanks for any advice.