Stock Price Prediction with Denoising Autoencoder and Transformers

Abstract

Predicting stock market price movement has been a challenging problem for time series forecasting due to its inherent volatility. The introduction of machine learning techniques, such as the use of a recurrent neural network (RNN), have since dominated the field for stock trend forecasting and stock price prediction. RNNs have inherent model limitations that are solved with the introduction of the transformer model, which has since been used in many sequential classification and generative tasks. This study demonstrates the viability of a transformer-based model on the field of stock price prediction by comparing the predictions of a transformer-based model with some RNN-based baseline models on the hourly closing price of Apple (AAPL) stock prices. A dimension reducing simple autoencoder was also incorporated into the transformer model to increase performance. Although RNN-based models are shown to outperform the transformer model in accuracy, the transformer model leads in directional stock accuracy. This demonstrates that the transformer is applicable to compete with RNN based models within the field, along with the incorporation of an autoencoder to further integrate the transformer’s power to this field.