Transformer Based Time-Series Forecasting for Stock
This addresses stock prediction for traders, but appears incremental as it adapts an existing method to a specific domain.
The authors tackled stock price forecasting by modeling it as a multivariate problem using a modified Transformer called Stockformer, but no concrete results or numbers are provided.
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.