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 …

Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …

Biodiesel has the potential to significantly contribute to making transportation fuels more
sustainable. Due to the complexity and nonlinearity of processes for biodiesel production …

Whether stock prices are predictable has been the center of debate in academia. In this
paper, we propose a hybrid model that combines a deep learning approach with a sentiment …

Feature selection is a critical and prominent task in machine learning. To reduce the
dimension of the feature set while maintaining the accuracy of the performance is the main …

Predicting the stock market has been done for a long time using traditional methods by
analyzing fundamental and technical aspects. With machine learning, stock market …

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 …

Achieving accurate stock market models can provide investors with tools for making better
data-based decisions. These models can help traders to reduce investment risk and select …

The success of portfolio construction depends primarily on the future performance of stock
markets. Recent developments in machine learning have brought significant opportunities to …

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 …