scholarly journals Mathematical and physical techniques of modeling and simulation of pattern recognition in the stock market

2021 ◽  
Vol 2073 (1) ◽  
pp. 012009
Author(s):  
O D Montoya ◽  
D D Narváez ◽  
C A Ramárez Vanegas
Keyword(s):  
Pattern Recognition ◽  
Stock Market ◽  
Modeling And Simulation ◽  
Good Prediction ◽  
Classification Methods ◽  
Basic Sciences ◽  
Physical Processes ◽  
Amount Of Information ◽  
Large Databases ◽  
Responsible Manner

Abstract The following article presents the analysis through mathematical and physical techniques of large databases, which are very common today, due to the large number of variables (especially in the information and physics industry) and the amount of information that results from a process, therefore an analysis is necessary that allows the Decision in a responsible manner, looking for scientific criteria that support said decisions, in our case a database of the forex system will be taken. Initially, a study and calculation of different measurements between the samples and their characteristics will be carried out to make a good prediction of the data and their behavior using different classification methods inspired by basic sciences. Below is an explanation of the techniques based on the analysis of data components and the correlations that exist between the variables, which is a technique widely used in physical processes to determine the correlations between variables.

scholarly journals Generating Trading Strategies in the Mexican Stock Market: A Pattern Recognition Approach

2018 ◽  
Vol 147 (12) ◽  
pp. 107-114
Author(s):  
David-Ricardo Montalván-Hernández ◽  
Ricardo Barrón-Fernández ◽  
Salvador Godoy-Calderón
Keyword(s):  
Pattern Recognition ◽  
Stock Market ◽  
Trading Strategies ◽  
Pattern Recognition Approach

scholarly journals Is soft independent modeling of class analogies a reasonable choice for supervised pattern recognition?

RSC Advances ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 10-21 ◽  
Author(s):  
Anita Rácz ◽  
Attila Gere ◽  
Dávid Bajusz ◽  
Károly Héberger
Keyword(s):  
Pattern Recognition ◽  
Data Sets ◽  
Classification Methods ◽  
Reasonable Choice

A thorough survey of classification data sets and a rigorous comparison of classification methods show the unambiguous superiority of other techniques over soft independent modeling of class analogies (SIMCA – one class modeling) for classification.

Interactive Classification Using a Granule Network

2011 ◽  
pp. 235-245
Author(s):  
Yan Zhao ◽  
Yiyu Yao
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Pattern Recognition ◽  
Classification System ◽  
Search Space ◽  
Classification Methods ◽  
Classification Problems ◽  
Computing Model ◽  
Tree Construction ◽  
Learning Data

Classification is one of the main tasks in machine learning, data mining, and pattern recognition. Compared with the extensively studied automation approaches, the interactive approaches, centered on human users, are less explored. This chapter studies interactive classification at 3 levels. At the philosophical level, the motivations and a process-based framework of interactive classification are proposed. At the technical level, a granular computing model is suggested for re-examining not only existing classification problems, but also interactive classification problems. At the application level, an interactive classification system (ICS), using a granule network as the search space, is introduced. ICS allows multi-strategies for granule tree construction, and enhances the understanding and interpretation of the classification process. Interactive classification is complementary to the existing classification methods.

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