The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …

In order to approximate the underlying process of temporal data, time series prediction has
been a hot research topic for decades. Developing predictive models plays an important role …

This paper aims to develop an innovative neural network approach to achieve better stock
market predictions. Data were obtained from the live stock market for real-time and off-line …

Recurrent neurons (and in particular LSTM cells) demonstrated to be efficient when used as
basic blocks to build sequence to sequence architectures, which represent the state-of-the …

Stock market is considered chaotic, complex, volatile and dynamic. Undoubtedly, its
prediction is one of the most challenging tasks in time series forecasting. Moreover existing …

Prediction of stock markets is a challenging problem because of the number of potential
variables as well as unpredictable noise that may contribute to the resultant prices …

The widespread use of mobile devices, as well as the increasing popularity of mobile
services has raised serious cybersecurity challenges. In the last years, the number of …

Deep learning is a framework for training and modelling neural networks which recently
have surpassed all conventional methods in many learning tasks, prominently image and …

In this study, the graphic images of time series of different chaotic systems are classified with
deep learning methods for the first time in the literature. For the classification, a …

Time series data analyze and prediction is very important to the study of nonlinear
phenomenon. Studies of time series prediction have a long history since last century, linear …