scholarly journals Hierarchical Temporal Memory Theory Approach to Stock Market Time Series Forecasting

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1630
Author(s):  
Regina Sousa ◽  
Tiago Lima ◽  
António Abelha ◽  
José Machado
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Technological Innovations ◽  
Theory Approach ◽  
Biological Functions ◽  
Automatic Learning ◽  
Temporal Memory ◽  
Learning Mechanism ◽  
Hierarchical Temporal Memory ◽  
The Brain

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.

Using the hierarchical temporal memory spatial pooler for short-term forecasting of electrical load time series

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
E.N. Osegi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Machine Intelligence ◽  
Series Data ◽  
Electrical Load ◽  
Short Term ◽  
Comparative Performance ◽  
Temporal Memory ◽  
Hierarchical Temporal Memory ◽  
Spatial Pooler

In this paper, an emerging state-of-the-art machine intelligence technique called the Hierarchical Temporal Memory (HTM) is applied to the task of short-term load forecasting (STLF). A HTM Spatial Pooler (HTM-SP) stage is used to continually form sparse distributed representations (SDRs) from a univariate load time series data, a temporal aggregator is used to transform the SDRs into a sequential bivariate representation space and an overlap classifier makes temporal classifications from the bivariate SDRs through time. The comparative performance of HTM on several daily electrical load time series data including the Eunite competition dataset and the Polish power system dataset from 2002 to 2004 are presented. The robustness performance of HTM is also further validated using hourly load data from three more recent electricity markets. The results obtained from experimenting with the Eunite and Polish dataset indicated that HTM will perform better than the existing techniques reported in the literature. In general, the robustness test also shows that the error distribution performance of the proposed HTM technique is positively skewed for most of the years considered and with kurtosis values mostly lower than a base value of 3 indicating a reasonable level of outlier rejections.

Pemodelan Data Time Series Garch(1,1) Untuk Pasar Saham Indonesia

2015 ◽  
Vol 11 (1) ◽  
pp. 13
Author(s):  
Elfa Rafulta ◽  
Roni Tri Putra
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Markets ◽  
Financial Data ◽  
International Stock Markets

This paper introduced a method pengklusteran for financial data. By using the model Heteroskidastity Generalized autoregressive conditional (GARCH), will be estimated distance between the stock market using GARCH-based distance. The purpose of this method is mengkluster international stock markets with different amounts of data.

Simulation of sports movement training based on machine learning and brain-computer interface

2020 ◽  
pp. 1-12
Author(s):  
Linuo Wang
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Joint Learning ◽  
Scientific Methods ◽  
Learning Framework ◽  
Brain Functions ◽  
Movement Training ◽  
Practical Effect ◽  
Machine Interface ◽  
The Brain

Injuries and hidden dangers in training have a greater impact on athletes ’careers. In particular, the brain function that controls the motor function area has a greater impact on the athlete ’s competitive ability. Based on this, it is necessary to adopt scientific methods to recognize brain functions. In this paper, we study the structure of motor brain-computer and improve it based on traditional methods. Moreover, supported by machine learning and SVM technology, this study uses a DSP filter to convert the preprocessed EEG signal X into a time series, and adjusts the distance between the time series to classify the data. In order to solve the inconsistency of DSP algorithms, a multi-layer joint learning framework based on logistic regression model is proposed, and a brain-machine interface system of sports based on machine learning and SVM is constructed. In addition, this study designed a control experiment to improve the performance of the method proposed by this study. The research results show that the method in this paper has a certain practical effect and can be applied to sports.

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