For this checkin, we also require you to write up a reflection including the following: Introduction: This can be copied from the proposal.
In the paper (link here), they used an LSTM model to predict future stock prices. We would like to adapt their paper to help predict future crypto prices. We believe this is a classification problem, as the paper predicts whether future prices will trend upwards or downwards.
Challenges: What has been the hardest part of the project you’ve encountered so far? Improving the accuracy of the model beyond a certain threshold; after 1 epoch, the model seems to get stuck at a certain loss value We are also in the process of figuring out a good accuracy function for us to use. At the moment we are utilizing a simple up-or-down prediction with sigmoid, but we would like to explore also predicting the percentage change in price, and therefore a continuous distribution for the predictions, and an accuracy threshold for the results.
Insights: Are there any concrete results you can show at this point? Yes, we have a basic LSTM model that predicts whether the price 10 minutes in the future will go up or down. At the moment we have a 1-epoch accuracy of around 70%.
How is your model performing compared with expectations? The model exceeds expectations, achieving a higher accuracy than initially anticipated.
Plan: Are you on track with your project? What do you need to dedicate more time to? Testing different model configurations Evaluating actual performance if we were to implement this in the real world Play around with different ways to evaluate accuracy (using percent change instead of just up or down)
What are you thinking of changing, if anything? Modifying the model architecture to achieve better results
Log in or sign up for Devpost to join the conversation.