Anticipating stock market trends is a challenging endeavor that requires a lot of attention
because correctly predicting stock prices can lead to significant rewards if the right …

In the financial market, there are a large number of indicators used to describe the change of
stock price, which provides a good data basis for our stock price forecast. Different stocks …

To boost the performance level of big data, data partitioning is considered to be as the
backbone of big data applications. In recent years, many researchers are focusing their work …

Selecting the best features in a dataset improves accuracy and efficiency of classifiers in a
learning process. Datasets generally have more features than necessary, some of them …

In this study, we propose a predictive model TabLSTM that combines machine learning
methods such as TabNet and Long Short-Term Memory Neural Network (LSTM) with a …

Feature selection is popular for obtaining small, interpretable, yet highly accurate prediction
models. Conventional feature-selection methods typically yield one feature set only, which …

This paper introduces a novel feature selection method, called Feature Selection based on
Importance Measures (FS-IM), to enhance the forecasting of crude oil returns. FS-IM …

Feature selection is popular for obtaining small, interpretable, yet highly accurate prediction
models. Conventional feature-selection methods typically yield one feature set only, which …

Data and information, being a critical part of the Internet, are vital to network security.
Intrusion Detection System (IDS) is required to preserve confidentiality, data integrity, and …

Stock price prediction models have attracted much research interest in recent years.
However, stock prices are high-dimensional financial time series. The application of artificial …