Book ChapterDOI
Stock Market Forecasting Using LASSO Linear Regression Model
Sanjiban Sekhar Roy,Dishant Mittal,Avik Basu,Ajith Abraham +3 more
- pp 371-381
TLDR
A Least Absolute Shrinkage and Selection Operator (LASSO) method based on alinear regression model is proposed as a novel method to predict financial market behavior and results indicate that the proposed model outperforms the ridge linear regression model.Abstract:
Predicting stock exchange rates is receiving increasing attention and is a vital financial problem as it contributes to the development of effective strategies for stock exchange transactions. The forecasting of stock price movement in general is considered to be a thought-provoking and essential task for financial time series’ exploration. In this paper, a Least Absolute Shrinkage and Selection Operator (LASSO) method based on a linear regression model is proposed as a novel method to predict financial market behavior. LASSO method is able to produce sparse solutions and performs very well when the numbers of features are less as compared to the number of observations. Experiments were performed with Goldman Sachs Group Inc. stock to determine the efficiency of the model. The results indicate that the proposed model outperforms the ridge linear regression model.read more
Citations
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Book ChapterDOI
A Deep Learning Based Artificial Neural Network Approach for Intrusion Detection
Sanjiban Sekhar Roy,Abhinav Mallik,Rishab Gulati,Mohammad S. Obaidat,Mohammad S. Obaidat,Parimala Venkata Krishna +5 more
TL;DR: The experimental results show that the accuracy of intrusion detection using Deep Neural Network is satisfactory and the potential capability of Deep Neural network as a classifier for the different types of intrusion attacks is checked.
Book
MICAI 2005 : advances in artificial intelligence : 4th Mexican International Conference on Artificial Intelligence, Monterrey, Mexico, November 14-18, 2005 : proceedings
TL;DR: In this article, the authors present an approach for dynamic split strategies in Constraint Problem Solving using Fuzzy Extension of Description Logic (DELL) for solving the Stereo Correspondence Problem.
Journal ArticleDOI
Toward Characterizing Blockchain-Based Cryptocurrencies for Highly Accurate Predictions
TL;DR: This article studies Bitcoin and Ethereum and explores features in their network that explain their price hikes and identifies key network features that help to determine the demand and supply dynamics in a cryptocurrency.
Journal ArticleDOI
Stock market forecasting by using a hybrid model of exponential fuzzy time series
Fatemeh Mirzaei Talarposhti,Hossein Javedani Sadaei,Rasul Enayatifar,Frederico Gadelha Guimarães,Maqsood Mahmud,Tayyebeh Eslami +5 more
TL;DR: The initial aim of this study is to propose a hybrid method based on exponential fuzzy time series and learning automata based optimization for stock market forecasting and this model has outperformed its counterparts in terms of accuracy.
Journal ArticleDOI
High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection
TL;DR: The regularization terms responsible for inducing coefficient shrinkage and variable selection leading to improved performance metrics of these regression models are discussed, making these modern, computational regression models valuable tools for analyzing high-dimensional problems.
References
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Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
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Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
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A Bayesian regularized artificial neural network for stock market forecasting
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Flexible neural trees ensemble for stock index modeling
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Proceedings ArticleDOI
Stock market value prediction using neural networks
TL;DR: The experimental results show that the application of MLP neural network is more promising in predicting stock value changes rather than Elman recurrent network and linear regression method.
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