Have you been struggling with your LSTM-based binary classifier in PyTorch, only to encounter early overfitting or no learning at all? You’re not alone. I’ve been there too, and I’m here to share some suggestions on optimizing your model architecture for better stock prediction results.
First, let’s break down the common issues: early overfitting, where your train loss goes down but val loss climbs after just a few epochs, and no learning, where your train and val loss stay flat around 0.693. Not ideal, right?
So, what can you do to improve your model? Here are a few ideas:
* Try tweaking your LSTM layer architecture. Two layers might not be enough, or maybe you need to adjust the number of hidden units.
* Experiment with different window sizes. Your current window size of 10 might be too small or too large for the task at hand.
* Consider adding more features to your input data. Are you only using the previous price to predict the up and down of a single stock? Maybe there are other factors at play that could improve your model’s accuracy.
* Regularization techniques, such as dropout or L1/L2 regularization, can help prevent overfitting.
* Don’t underestimate the power of hyperparameter tuning. Try different learning rates, batch sizes, or optimizers to find the right combination for your model.
By implementing these suggestions, you might just find that your LSTM model starts performing better and giving you more accurate stock predictions.