This study aims to contribute on the forecasting literature in stock return for emerging markets. We use Autometrics to select relevant predictors among macroeconomic, microeconomic and technical variables. We develop ...

n this paper, we propose a novel support vector regression (SVR) approach for time series analysis. An efficient forward feature selection strategy has been designed for dealing with high-frequency time series with multiple ...

This study aims to contribute on the forecasting literature in stock return for emerging markets. We use Autometrics to select relevant predictors among macroeconomic, microeconomic and technical variables. We develop ...

This paper analyzes idiosyncratic and common determinants of commodity prices by adopting a time series-cross section approach. Different from the previous empirical literature, we allow for long-run and short-run effects, ...

Recent works for mature markets on covered interest parity suggest that deviations are mean reverting but persistent particularly after 2008 crisis (Du et al., 2018). Our study aims to contribute to the literature by ...

A key element to enable the analysis and accessing to radio broadcast content is the development of automatic speech-to-text systems. The building of these systems has been possible given the current available of different ...

The most common method to validate a DEVS model against the requirements is to simulate it several times under different conditions, with some simulation tool. The behavior of the model is compared with what the system is ...

The automatic selection of feature variables is a task of increasing interest in the field of pattern recognition. Neural models have recently been used for this purpose. Among other models, the adaptive-subspace SOM (ASSOM) ...

Recognizing Textual Entailment (RTE) is an NLP task aimed at detecting whether the meaning of a given piece of text entails the meaning of another one. Despite its relevance to many NLP areas, it has been scarcely explored ...