A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting
From exchanging budgetary instruments to tracking individual spending plans to detail a
business's profit, money-related organisations utilise computational innovation day by day …
business's profit, money-related organisations utilise computational innovation day by day …
Computing graph neural networks: A survey from algorithms to accelerators
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …
years owing to their capability to model and learn from graph-structured data. Such an ability …
[HTML][HTML] Stock market prediction using machine learning techniques: a decade survey on methodologies, recent developments, and future directions
With the advent of technological marvels like global digitization, the prediction of the stock
market has entered a technologically advanced era, revamping the old model of trading …
market has entered a technologically advanced era, revamping the old model of trading …
A systematic review of fundamental and technical analysis of stock market predictions
The stock market is a key pivot in every growing and thriving economy, and every investment
in the market is aimed at maximising profit and minimising associated risk. As a result …
in the market is aimed at maximising profit and minimising associated risk. As a result …
Financial time series forecasting model based on CEEMDAN and LSTM
J Cao, Z Li, J Li - Physica A: Statistical mechanics and its applications, 2019 - Elsevier
In order to improve the accuracy of the stock market prices forecasting, two hybrid
forecasting models are proposed in this paper which combine the two kinds of empirical …
forecasting models are proposed in this paper which combine the two kinds of empirical …
CNNpred: CNN-based stock market prediction using a diverse set of variables
E Hoseinzade, S Haratizadeh - Expert Systems with Applications, 2019 - Elsevier
Feature extraction from financial data is one of the most important problems in market
prediction domain for which many approaches have been suggested. Among other modern …
prediction domain for which many approaches have been suggested. Among other modern …
NSE stock market prediction using deep-learning models
M Hiransha, EA Gopalakrishnan, VK Menon… - Procedia computer …, 2018 - Elsevier
The neural network, one of the intelligent data mining technique that has been used by
researchers in various areas for the past 10 years. Prediction and analysis of stock market …
researchers in various areas for the past 10 years. Prediction and analysis of stock market …
Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies
We offer a systematic analysis of the use of deep learning networks for stock market analysis
and prediction. Its ability to extract features from a large set of raw data without relying on …
and prediction. Its ability to extract features from a large set of raw data without relying on …
[HTML][HTML] Survey of feature selection and extraction techniques for stock market prediction
In stock market forecasting, the identification of critical features that affect the performance of
machine learning (ML) models is crucial to achieve accurate stock price predictions. Several …
machine learning (ML) models is crucial to achieve accurate stock price predictions. Several …
Fine-tuned support vector regression model for stock predictions
In this paper, a new machine learning (ML) technique is proposed that uses the fine-tuned
version of support vector regression for stock forecasting of time series data. Grid search …
version of support vector regression for stock forecasting of time series data. Grid search …