Regularized dynamic self-organized neural network inspired by the immune algorithm for financial time series prediction

Hussain, Abir Jaafar; Al‐Jumeily, Dhiya; Alaskar, Haya; Radi, Naeem

doi:10.1016/j.neucom.2015.01.109

Cited by 31 publications

(7 citation statements)

References 34 publications

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“…One of the critical aspects of AI is the application of natural language processing (NLP). NLP is a field of artificial intelligence (AI) that provides computers the capacity to read text and spoken language in a manner similar to that of humans which supports in analysing the data, offers more potential in integrating the large volume of information, supports in predicting the pattern, and extrapolates the information for effective analysis on the broader market [6]. Machine learning can be applied to various financial decision making for the management as it enables making extensive analysis covering regression, vector machines, etc.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Business Machine Learning in Making Effective Financial Decisions Using Structural Equation Model (SEM)

Sujith

Khan

Dornadula³

et al. 2022

Journal of Food Quality

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Globally, organisations are focused on deriving more value from the data which has been collected from various sources. The purpose of this research is to examine the key components of machine learning in making efficient financial decisions. The business leaders are now faced with huge volume of data, which needs to be stored, analysed, and retrieved so as to make effective decisions for achieving competitive advantage. Machine learning is considered to be the subset of artificial intelligence which is mainly focused on optimizing the business process with lesser or no human interventions. The ML techniques enable analysing the pattern and recognizing from large data set and provide the necessary information to the management for effective decision making in different areas covering finance, marketing, supply chain, human resources, etc. Machine learning enables extracting the quality patterns and forecasting the data from the data base and fosters growth; the machine learning enables transition from the physical data to electronically stored data, enables enhancing the memory, and supports with financial decision making and other aspects. This study is focused on addressing the application of machine learning in making the effective financial decision making among the companies; the application of ML has emerged as a critical technology which is being applied in the current competitive market, and it has offered more opportunities to the business leaders in leveraging the large volume of data. The study is intended to collect the data from employees, managers, and business leaders in various industries to understand the influence of machine learning in financial decision making .

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Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Business Machine Learning in Making Effective Financial Decisions Using Structural Equation Model (SEM)

Sujith

Khan

Dornadula³

et al. 2022

Journal of Food Quality

View full text Add to dashboard Cite

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“…This study finds that genetic programming marginally outperforms in trading. The more recent Hussain et al (2016) study applies recurrent neural networks, which are a form of reinforcement learning that is classified as deep learning. The study's particular technique is not to showcase the ability of recurrent neural networks to predict financial time series, but rather a new regularization technique developed to allow better organization of the hidden layer in the neural network.…”

Section: Investment Analysis Topicsmentioning

confidence: 99%

“…who use a generalized regression neural network to predict from two technical indicators that combine volume and price signals, and Tilakaratne et al (2007) who use intermarket indicators.Variations of reinforcement learning for technical analysis are applied byDempster et al (2001) andHussain et al (2016) Dempster et al (2001). races reinforcement learning and genetic programming for popular technical indicators used in forex trading (such as relative strength and momentum oscillators).…”

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confidence: 99%

Machine Learning and Finance

Khatib¹

2021

Int J Innov Educ Res

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The aim of this paper is provide a first comprehensive structuring of the literature applying machine learning to finance. We use a probabilistic topic modelling approach to make sense of this diverse body of research spanning across the disciplines of finance, economics, computer sciences, and decision sciences. Through the topic modelling approach, a Latent Dirichlet Allocation Technique (LDA), we can extract the 14 coherent research topics that are the focus of the 6,148 academic articles during the years 1990-2019 analysed. We first describe and structure these topics, and then further show how the topic focus has evolved over the last two decades. Our study thus provides a structured topography for finance researchers seeking to integrate machine learning research approaches in their exploration of finance phenomena. We also showcase the benefits to finance researchers of the method of probabilistic modelling of topics for deep comprehension of a body of literature, especially when that literature has diverse multi-disciplinary actors.

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“…A time series is a collection of observations of data items taken sequentially during a specific time period. Time series basically refers to an arrangement of observations over time intervals and measured frequently over successive times [16]. There are some examples of time series like the price of a stock over successive days, sizes of queries to a database system, sizes of video frames and sizes of packets over the network.…”

Section: Neural Network Techniques For Time Series Forecastingmentioning

confidence: 99%

Neural Network Techniques for Time Series Prediction: A Review

Mushtaq

Akram

Aamir

et al. 2019

JOIV : Int. J. Inform. Visualization

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It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series. Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

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Regularized dynamic self-organized neural network inspired by the immune algorithm for financial time series prediction

Cited by 31 publications

References 34 publications

A Comparative Analysis of Business Machine Learning in Making Effective Financial Decisions Using Structural Equation Model (SEM)

A Comparative Analysis of Business Machine Learning in Making Effective Financial Decisions Using Structural Equation Model (SEM)

Machine Learning and Finance

Neural Network Techniques for Time Series Prediction: A Review

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