In this research, we propose an enhanced Grey Wolf Optimizer (GWO) for designing the
evolving Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) networks for …

With the development of Internet techniques, data volumes are doubling every two years,
faster than predicted by Moore's Law. Big Data Analytics becomes particularly important for …

Time series data contains temporal ordering, which makes its feature selection different from
the normal feature selection. Feature selection in multivariate time series has two tasks …

Stock exchanges are considered major players in financial sectors of many countries. Most
Stockbrokers, who execute stock trade, use technical, fundamental or time series analysis in …

In this paper a novel application of a particular type of spiking neural network, a
Polychronous Spiking Network, was used for financial time series prediction. It is argued that …

Several models have been presented to solve the financial time series forecasting problem.
However, even with sophisticated techniques, a dilemma arises from all these models …

Feature selection is a primary exercise to tackle any forecasting task. Machine learning
algorithms used to predict any variable can improve their performance by lessening their …

Financial-economic time series distinguishes from other time series because they contain a
portion of uncertainity. Because of this, statistical theory and methods play important role in …

Expert systems for trading signal detection have received considerable attention in recent
years. In financial trading systems, investors' main concern is determining the best time to …

Feature selection is a known technique to preprocess the data before performing any data
mining task. In multivariate time series (MTS) prediction, feature selection needs to find both …