In the graph below, I train for 400 epochs and I use a simple hold out validation set representing the last 10% of the training set, rather than a full cross validation at the moment, so it is not alarming that the validation loss is less than the training. Dealing with such a Model: Data Preprocessing: Standardizing and Normalizing the data. To help the LSTM model to converge faster it is important to scale the data. PyTorch: Large non-decreasing LSTM training loss Here is a simple formula: α ( t + 1) = α ( 0) 1 + t m. Where a is your learning rate, t is your iteration number and m is a coefficient that identifies learning rate decreasing speed. . Our post will focus on both how to apply deep learning to time series forecasting, and how to . How to use the Keras API to add weight regularization to an MLP, CNN, or LSTM neural network. How to use Learning Curves to Diagnose Machine Learning Model Performance The gray indicates the data that we'll set aside for final testing. How to Use Weight Decay to Reduce Overfitting of Neural Network in Keras The curve of loss are shown in the following figure: It also seems that the validation loss will keep going up if I train the model for more epochs. I just shifted from keras and finding some difficulty to validate my code. There are many other options as well to reduce overfitting, assuming you are using Keras, visit this link. Recurrent Neural Network Training Loss does not decrease past a certain ... Just at the end adjust the training and the validation size to get the best result in the test set. It has an LSTMCell unit and a linear layer to model a sequence of a time series. LSTM Accuracy unchanged while loss decrease in Lstm

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