One of the most difficult problems analysts and decision-makers may face is how to improve the forecasting and predicting of financial time series. However, several efforts were made to develop more accurate and reliable forecasting methods. The main purpose of this study is to use technical...

Background: Improving financial time series forecasting is one of the most challenging and vital issues facing numerous financial analysts and decision makers. Given its direct impact on related decisions, various attempts have been made to achieve more accurate and reliable forecasting results,...

Background: Improving financial time series forecasting is one of the most challenging and vital issues facing numerous financial analysts and decision makers. Given its direct impact on related decisions, various attempts have been made to achieve more accurate and reliable forecasting results,...

The purpose of this paper is to make a quantitative and qualitative critical analyse regarding the three important aspects of stock market evolution. First, the forecasting problems are presented and analyse in order to establish the main problems and the potential solutions. Second, the...

Accurate prediction of stock market behavior is a challenging issue for financial forecasting. Artificial neural networks, such as multilayer perceptron have been established as better approximation and classification models for this domain. This study proposes a chemical reaction optimization...

Extreme learning machine (ELM) allows for fast learning and better generalization performance than conventional gradient-based learning. However, the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network...

Deep learning has substantially advanced the state of the art in computer vision, natural language processing, and other fields. The paper examines the potential of deep learning for exchange rate forecasting. We systematically compare long short-term memory networks and gated recurrent units to...

Deep learning has substantially advanced the state-of-the-art in computer vision, natural language processing and other elds. The paper examines the potential of contemporary recurrent deep learning architectures for nancial time series forecasting. Considering the foreign exchange market as...