I am trying to train a RNN Network for stock price prediction for my Master Thesis. I have additional input values (6), not just the stock prices by itself.
Using an LSTM Network with the “optimal” structure based on Hyperparameter Tuning with Keras Tuner, i observed a significant increase in the losses for training and validation in my case after 4000 Epochs.
My dataset consists of about 12 000 datapoints and i use the Adam optimizer with mean_absolute_error loss function
The Network is quite deep with the following layers:
The standard interpretation is that your network is starting to overtrain after 4000/8000 epochs.
Do you have a separate holdout “test” set? The current recommended practice is to have three datasets: training, validation, and test. You run training with the training and validation datasets, and stop when validation starts to fail. Then run the optimally valid model on the holdout test set. The validation and test loss and accuracy should roughly match.
I do not have a test set, but can easily generate one. But what difference does it make to the validation set?
I would have expected to see overtraining as the training loss to stay low, but just the validation loss to increase.
If i am wrong, could you give me a rough explanation, i need it for my master thesis.