Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/74348
Título: | Hierarchical temporal memory theory approach to stock market time series forecasting |
Autor(es): | Sousa, Ana Regina Coelho Lima, Tiago Abelha, António Machado, José Manuel |
Palavras-chave: | Time series forecasting HTM Regression Machine intelligence Deep learning |
Data: | 8-Jul-2021 |
Editora: | Multidisciplinary Digital Publishing Institute (MDPI) |
Revista: | Electronics |
Citação: | Sousa, R.; Lima, T.; Abelha, A.; Machado, J. Hierarchical Temporal Memory Theory Approach to Stock Market Time Series Forecasting. Electronics 2021, 10, 1630. https://doi.org/10.3390/electronics10141630 |
Resumo(s): | 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. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/74348 |
DOI: | 10.3390/electronics10141630 |
e-ISSN: | 2079-9292 |
Versão da editora: | https://www.mdpi.com/2079-9292/10/14/1630 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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electronics-10-01630.pdf | 4,23 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons