Stock market prediction has been a classical yet challenging problem, with the attention from
both economists and computer scientists. With the purpose of building an effective prediction …

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 …

Multivariate time-series forecasting plays a crucial role in many real-world applications. It is
a challenging problem as one needs to consider both intra-series temporal correlations and …

The nature of stock market movement has always been ambiguous for investors because of
various influential factors. This study aims to significantly reduce the risk of trend prediction …

Stock market prediction has always caught the attention of many analysts and researchers.
Popular theories suggest that stock markets are essentially a random walk and it is a fool's …

The prediction of stock groups values has always been attractive and challenging for
shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper …

Supervised machine learning and its algorithm is an emerging trend for the prediction of
mechanical properties of concrete. This study uses an ensemble random forest (RF) and …

The price forecasting of the digital currencies in the financial market is of great importance,
especially after the recent global economic crises. Due to the nonlinear dynamics, which is …

Fly ash (FA) is a residual from thermal industries that has been effectively utilized in the
production of FA‐based geopolymer concrete (FGPC). To avoid time‐consuming and costly …

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 …