[HTML][HTML] Machine learning techniques and data for stock market forecasting: A literature review
In this literature review, we investigate machine learning techniques that are applied for
stock market prediction. A focus area in this literature review is the stock markets …
stock market prediction. A focus area in this literature review is the stock markets …
Mixture of experts: a literature survey
S Masoudnia, R Ebrahimpour - Artificial Intelligence Review, 2014 - Springer
Mixture of experts (ME) is one of the most popular and interesting combining methods, which
has great potential to improve performance in machine learning. ME is established based on …
has great potential to improve performance in machine learning. ME is established based on …
Financial forecasting using ANFIS networks with quantum-behaved particle swarm optimization
To be successful in financial market trading it is necessary to correctly predict future market
trends. Most professional traders use technical analysis to forecast future market prices. In …
trends. Most professional traders use technical analysis to forecast future market prices. In …
Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting
Highly accurate interval forecasting of a stock price index is fundamental to successfully
making a profit when making investment decisions, by providing a range of values rather …
making a profit when making investment decisions, by providing a range of values rather …
Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations
J Wang, J Wang - Energy, 2016 - Elsevier
In an attempt to improve the forecasting accuracy of crude oil price fluctuations, a new neural
network architecture is established in this work which combines Multilayer perception and …
network architecture is established in this work which combines Multilayer perception and …
Financial time series prediction using elman recurrent random neural networks
J Wang, J Wang, W Fang, H Niu - Computational intelligence …, 2016 - Wiley Online Library
In recent years, financial market dynamics forecasting has been a focus of economic
research. To predict the price indices of stock markets, we developed an architecture which …
research. To predict the price indices of stock markets, we developed an architecture which …
Forecasting stochastic neural network based on financial empirical mode decomposition
J Wang, J Wang - Neural Networks, 2017 - Elsevier
In an attempt to improve the forecasting accuracy of stock price fluctuations, a new one-step-
ahead model is developed in this paper which combines empirical mode decomposition …
ahead model is developed in this paper which combines empirical mode decomposition …
Short-term forecasting for energy consumption through stacking heterogeneous ensemble learning model
MA Khairalla, X Ning, NT Al-Jallad, MO El-Faroug - Energies, 2018 - mdpi.com
In the real-life, time-series data comprise a complicated pattern, hence it may be challenging
to increase prediction accuracy rates by using machine learning and conventional statistical …
to increase prediction accuracy rates by using machine learning and conventional statistical …
Predicting methane concentration in longwall regions using artificial neural networks
Methane, which is released during mining exploitation, represents a serious threat to this
process. This is because the gas may ignite or cause an explosion. Both of these …
process. This is because the gas may ignite or cause an explosion. Both of these …
Daily natural gas consumption forecasting based on a structure-calibrated support vector regression approach
An accurate forecast of natural gas (NG) consumption is of vital importance for economical
and reliable operation of the distributive NG networks. In this paper, a structure-calibrated …
and reliable operation of the distributive NG networks. In this paper, a structure-calibrated …