Learning Pattern Recognition Techniques Applied to Stock Market Forecasting

1975 ◽  
Vol SMC-5 (6) ◽  
pp. 583-594 ◽  
Author(s):  
Jerry Felsen
Keyword(s):  
Pattern Recognition ◽  
Stock Market ◽  
Pattern Recognition Techniques ◽  
Learning Pattern ◽  
Stock Market Forecasting

Neuroimaging of bipolar disorder

2020 ◽  
pp. 744-756
Author(s):  
Mary L Phillips ◽  
Wayne C Drevets
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Bipolar Disorder ◽  
Pattern Recognition ◽  
Psychiatric Disorders ◽  
Multimodal Neuroimaging ◽  
Protein Marker ◽  
Pattern Recognition Techniques ◽  
Learning Pattern ◽  
Youth At Risk

This chapter discusses findings from recent major neuroimaging studies of bipolar disorder to provide a better understanding of larger-scale neural circuitry, neurotransmitter concentration, bioenergetic process, and protein marker abnormalities in the disorder. The chapter also reviews findings from newer areas of neuroimaging research, including studies comparing bipolar disorder with other major psychiatric disorders, multimodal neuroimaging studies, studies of youth with, and youth at risk for, the disorder, and studies using machine-learning pattern recognition techniques. These studies are paving the way for identification of robust and objective neural biomarkers of bipolar disorder that can ultimately have clinical utility.

EEG-Based Emotion Recognition Using Different Neural Network and Pattern Recognition Techniques A Review

2019 ◽  
Vol 7 (1) ◽  
pp. 615-618
Author(s):  
Y. M. Rajput ◽  
S. Abdul Hannan ◽  
M. Eid Alzahrani ◽  
Ramesh R. Manza ◽  
Dnyaneshwari D. Patil
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Emotion Recognition ◽  
Pattern Recognition Techniques

Maximal Overlap Discrete Wavelet Transform, Graph Theory And Backpropagation Neural Network In Stock Market Forecasting

2018 ◽  
Vol 5 (1) ◽  
pp. 41-46
Author(s):  
Rosalina Rosalina ◽  
Hendra Jayanto
Keyword(s):  
Neural Network ◽  
Graph Theory ◽  
Wavelet Transform ◽  
Stock Market ◽  
Discrete Wavelet Transform ◽  
Stock Price ◽  
Moving Average ◽  
Discrete Wavelet ◽  
Stock Market Forecasting ◽  
Artificial Neural

The aim of this paper is to get high accuracy of stock market forecasting in order to produce signals that will affect the decision making in the trading itself. Several experiments by using different methodologies have been performed to answer the stock market forecasting issues. A traditional linear model, like autoregressive integrated moving average (ARIMA) has been used, but the result is not satisfactory because it is not suitable for model financial series. Yet experts are likely observed another approach by using artificial neural networks. Artificial neural network (ANN) are found to be more effective in realizing the input-output mapping and could estimate any continuous function which given an arbitrarily desired accuracy. In details, in this paper will use maximal overlap discrete wavelet transform (MODWT) and graph theory to distinguish and determine between low and high frequencies, which in this case acted as fundamental and technical prediction of stock market trading. After processed dataset is formed, then we will advance to the next level of the training process to generate the final result that is the buy or sell signals given from information whether the stock price will go up or down.

Gravity Models in International Trade: An Exploration in Econo-Physics

2021 ◽  
pp. 227797872198992
Author(s):  
Iman Pal ◽  
Saibal Kar
Keyword(s):  
Pattern Recognition ◽  
International Trade ◽  
Present Article ◽  
Gravity Models ◽  
Scientific Applications ◽  
Physical Sciences ◽  
Trade Patterns ◽  
Pattern Recognition Techniques ◽  
The Subject ◽  
Jel Classifications

Several strands of the static and dynamic theoretical constructs and the empirical applications in the subject of economics owe substantially to the well-known principles of physical sciences. The present article explores as to how the development of the popular gravity models in international trade can be traced back to Newton’s law of gravitation, and to both Ohm’s Law and Kirchhoff’s Law of current electricity, as well as to the pattern recognition techniques commonly deployed in scientific applications. In addition to surveying these theoretical analogies, the article also offers numerical applications for observed trade patterns between India and a set of countries. JEL Classifications: F41, F42, C61, F47

A simulation model for evaluating the effectiveness of various stock market strategies

SIMULATION ◽  
1969 ◽  
Vol 13 (6) ◽  
pp. 299-305 ◽  
Author(s):  
P. Krolak ◽  
R. Berquist ◽  
R. Conn ◽  
H. Gilliland
Keyword(s):  
Stock Market ◽  
Simulation Model ◽  
Review Of The Literature ◽  
Preliminary Results ◽  
Stock Market Forecasting ◽  
Market Strategies

This paper develops a simulation model which can be used to investigate a wide variety of stock market invest ment strategies. A brief review of the literature of stock market forecasting is given. The paper describes the de tails that any simulation of a stock market investor would have to include if the model is to be realistically com pared to the performance of real investors. An outline of the necessary features of any program which is to be used to investigate may different combinations of invest ment strategies and forecasting devices is also given. The program is described in detail and a few preliminary results are given.

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