To the naked eye, stock prices are considered chaotic, dynamic, and unpredictable. Indeed, it is one of the most difficult forecasting tasks that hundreds of millions of retail traders and professional traders around the world try to do every second even before the market opens. With recent advances in the development of machine learning and the amount of data the market generated over years, applying machine learning techniques such as deep learning neural networks is unavoidable. In this work, we modeled the task as a multivariate forecasting problem, instead of a naive autoregression problem. The multivariate analysis is done using the attention mechanism via applying a mutated version of the Transformer, "Stockformer", which we created.

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arXiv:2502.09625, January 2025.

@article{li:arxiv25, title={Transformer Based Time-Series Forecasting for Stock}, author={Shuozhe Li and Zachery B Schulwolf and Risto Miikkulainen}, journal={arXiv:2502.09625}, month={January}, url="http://nn.cs.utexas.edu/?li:arxiv25", year={2025} }