I've trained an LSTM network to predict time series data however i'm quite new to LSTMs and am unsure if the model has overfit.
I know that an increasing validation loss relative to a decreasing training loss is the main way you determine overfitting and underfitting along with plotting the real values against the predicted values, so that is what I did. but im getting what seem like contrasting results. Or am I wrong in thinking so?
here's my model which ive made in python, TensorFlow/Keras
lstm_model = tf.keras.models.Sequential([ tf.keras.layers.LSTM(32, return_sequences=True), tf.keras.layers.Dense(units=1) ])
and here's the metrics of my last 5 epochs ( I ran the data on a total of 15 epochs i've tried increasing it to 20 and decreasing it to 10 with similar results. my loss function is MSE.
Epoch 11/15 - 17s 146ms/step - loss: 4.4813e-04 - mean_absolute_error: 0.0138 - val_loss: 0.0016 - val_mean_absolute_error: 0.0308 Epoch 12/15 - 17s 149ms/step - loss: 4.1059e-04 - mean_absolute_error: 0.0133 - val_loss: 0.0015 - val_mean_absolute_error: 0.0295 Epoch 13/15 - 17s 146ms/step - loss: 3.8052e-04 - mean_absolute_error: 0.0128 - val_loss: 0.0014 - val_mean_absolute_error: 0.0288 Epoch 14/15 - 17s 152ms/step - loss: 3.5690e-04 - mean_absolute_error: 0.0125 - val_loss: 0.0013 - val_mean_absolute_error: 0.0279 Epoch 15/15 - 18s 156ms/step - loss: 3.3720e-04 - mean_absolute_error: 0.0122 - val_loss: 0.0013 - val_mean_absolute_error: 0.0276
I personally think that this isn't very overfit, because while there is a relatively big difference between val_loss and training loss (loss), they both are still fairly low.
But then when I looked at the plot of real values against predictions, it felt a little too accurate, because on short time intervals such as 0-20 days, there's a decent amount of errors that the model is making, however when I take large time intervals such as one year, the predictions seem to be a little too accurate, the plots ive attached below are of the model's predictions on 10 and 365 days respectively.
red : predicted value
blue: real value
So what do I conclude from this? The val loss and training loss seems to be okay but the plot seems to be too good. I assume that im wrong in thinking that the val_loss and training loss are stable, if so what kind of difference should both of them have when I should understand that its overfit, if not then is this just a good model?
p.s I sincerely apologise if this forum is the wrong place for this question or if ive done something wrong, its my first time posting to cross validated