Hidformer: Transformer-Style Neural Network in Stock Price Forecasting
It addresses stock price forecasting for financial analysts and traders, but appears incremental as it applies a slightly modified existing model to a specific domain.
This paper investigates whether the Hidformer model, a Transformer-based neural network, can effectively forecast stock prices by integrating technical analysis with machine learning, finding it offers potential improvements for algorithmic trading strategies.
This paper investigates the application of Transformer-based neural networks to stock price forecasting, with a special focus on the intersection of machine learning techniques and financial market analysis. The evolution of Transformer models, from their inception to their adaptation for time series analysis in financial contexts, is reviewed and discussed. Central to our study is the exploration of the Hidformer model, which is currently recognized for its promising performance in time series prediction. The primary aim of this paper is to determine whether Hidformer will also prove itself in the task of stock price prediction. This slightly modified model serves as the framework for our experiments, integrating the principles of technical analysis with advanced machine learning concepts to enhance stock price prediction accuracy. We conduct an evaluation of the Hidformer model's performance, using a set of criteria to determine its efficacy. Our findings offer additional insights into the practical application of Transformer architectures in financial time series forecasting, highlighting their potential to improve algorithmic trading strategies, including human decision making.