The main goal of this research paper is to apply a deep neural network model for time series
forecasting of environmental variables. Accurate forecasting of snow cover and NDVI are …

Financial market predictions utilize historical data to anticipate future stock prices and
market trends. Traditionally, these predictions have focused on the statistical analysis of …

This paper reviews the application of artificial neural network (ANN) models to time series
prediction tasks. We begin by briefly introducing some basic concepts and terms related to …

Nowadays, e-learning plays a very significant role in a global education system. E-learning
is an effective way, which offers convenient and more benefits to users. In addition, it …

Nowadays, people show more and more enthusiasm for applying machine learning
methods to finance domain. Many scholars and investors are trying to discover the mystery …

For many years, economists have been using statistical tools to estimate parameters of
macroeconomic models. Forecasting plays a major role in macroeconomic planning and it is …

Using time series data to predict future sales changes of products is of great significance to
every retailing company in terms of management and planning of resources. In order to find …

Day trading has become an important topic of discussion in the last decades, especially with
regard to computer program trading or the increasing trend of high-frequency transactions …

In this paper, we investigate the volatility dynamics of EUR/GBP currency using statistical as
well as the neural network approach which is an alternative way for time series modelling …

For steer-by-wire systems, the steering feedback must be generated artificially due to the
system characteristics. Classical control concepts require operating-point driven …