Hierarchical temporal memory theory approach to stock market time series forecasting

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 prediction...

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Bibliographic Details
Published in:Electronics (Basel) 2021-07, Vol.10 (14), p.1-15
Main Authors: Sousa, Ana Regina Coelho, Lima, Tiago, Abelha, António, Machado, José Manuel
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Datasets
Deep learning
Economic forecasting
Forecasting
HTM
Learning curves
Machine intelligence
Machine learning
Memory
Neurons
Predictions
Prices
Principal components analysis
Regression
Science & Technology
Securities markets
Semantics
Smart cities
Sparsity
Stock exchanges
Time series
Time series forecasting
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Summary: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. This work is funded by “FCT—Fundação para a Ciência e Tecnologia” within the R&D Units Project Scope: UIDB/00319/2020. The grant of R.S. is supported by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internalization Programme (COMPETE 2020). [Project n. 039479. Funding Reference: POCI-01-0247- FEDER-039479].
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics10141630