Hierarchical Temporal Memory Theory Approach to Stock Market Time Series Forecasting

Electronics

Regina Sousa¹  Tiago Lima¹  António Abelha¹  José Machado¹ 

[1] ALGORITMI Research Center, School of Engineering, Gualtar Campus, University of Minho, 4710-057 Braga, Portugal;

关键词: time series forecasting;    HTM;    regression;    machine intelligence;    deep learning;   

DOI  :  10.3390/electronics10141630

来源: DOAJ

【 摘 要 】

Over the years, and with the emergence of various technological innovations, the relevance of automatic learning methods has increased exponentially, and they now play a key role in society. More specifically, Deep Learning (DL), with the ability to recognize audio, image, and time series predictions, has helped to solve various types of problems. This paper aims to introduce a new theory, Hierarchical Temporal Memory (HTM), that applies to stock market prediction. HTM is based on the biological functions of the brain as well as its learning mechanism. The results are of significant relevance and show a low percentage of errors in the predictions made over time. It can be noted that the learning curve of the algorithm is fast, identifying trends in the stock market for all seven data universes using the same network. Although the algorithm suffered at the time a pandemic was declared, it was able to adapt and return to good predictions. HTM proved to be a good continuous learning method for predicting time series datasets.

【 授权许可】

Unknown