The paper presents examples of application of error correction models (ECM) in forecasting daily changes of market volatility recorded on currency options markets in Poland, Hungary and South Africa. The models are based on the observed correlation between daily changes of spot rates and daily...

In this study, we treat the seasonal variation in monthly time series in the context of the Western-European tourism demand for Tunisia, by presenting different techniques of detection of seasonality and the parametric and non-parametric approaches of seasonal adjustment. Then, we compare the...

This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in...

The paper makes an attempt to provide estimation of foreign tourism demand by 2014, by investigating the case of Macedonia. The author applies the Box-Jenkins methodology and tests several alternative specifications in the modeling of original time series. Upon the outcomes of standard...

Two different models are developed to forecast the total tourist arrivals to Turkey using monthly data for the period 2002-2013. The results of the study show that two models provide accurate predictions but the seasonal autoregressive integrated moving average model produces more accurate...

This paper aims to compare the performance of different Artificial Neural Networks techniques for tourist demand forecasting. We test the forecasting accuracy of three different types of architectures: a multi-layer perceptron, a radial basis function and an Elman network. We also evaluate the...

This paper improves short-term forecasting models of monthly tourism arrivals by estimating and evaluating a time-series model with exogenous regressors (ARIMA-X) using a case of Aruba, a small open tourism-dependent economy. Given importance of the US market for Aruba, it investigates...

This paper uses support vector regressions (SVRs) and Google search data to test whether observing internet habits can provide insights on trends in tourist arrivals to Barbados. We find evidence that Google Trends data may be used to (informally) pick up changing patterns and trends in tourist...

The present study investigates the performance of the k nearest neighbor (kNN) forecasts in the context of European tourism demand. The forecasting performance of neural networks is examined across different parameterizations of the kNN model. The selection of the most appropriate kNN...