Recurrent neural networks (RNNs) are types of artificial neural networks (ANNs) that are well suited to forecasting and … sequence classification. They have been applied extensively to forecasting univariate financial time series, however their …

This paper aims to explore the forecasting accuracy of RON/USD exchange rate structural models with monetary …

The literature on exchange rate forecasting is vast. Many researchers have tested whether implications of theoretical … literature on exchange rate forecasting is scarce. This article fills this gap by testing whether non-linear time series models … naive random walk in exchange rate forecasting contest …

forecasting. We test the forecasting accuracy of three different types of architectures: a multi-layer perceptron, a radial basis …. These results indicate the potential existence of instabilities when using dynamic networks for forecasting purposes. We … also find that for higher memories, the forecasting performance obtained for longer horizons improves, suggesting the …

This study compares the performance of different Artificial Neural Networks models for tourist demand forecasting in a … multiple-output framework. We test the forecasting accuracy of three different types of architectures: a multi-layer perceptron …-of-sample forecasting comparison. When comparing the forecasting accuracy of the different techniques for each visitor market and for …

In many macroeconomic forecasting applications factor models are used to cope with large datasets. This study aligns … variational autoencoders with macroeconomic factor modeling and proposes an extension to adapt this framework for forecasting … forecasting power. The results suggest significant improvements in the forecasting accuracy of four major US macroeconomic time …