Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Neural networks

Recurrent Neural Networks for Time Series Forecasting: Current status and future directions

A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting

Smart hospitality—Interconnectivity and interoperability towards an ecosystem

http://diposit.ub.edu/dspace/bitstream/2445/54587/1/628233.pdf

Forecasting tourism demand to Catalonia: Neural networks vs. time series models

http://diposit.ub.edu/dspace/bitstream/2445/45246/5/548959.pdf

Business and consumer expectations and macroeconomic forecasts

This paper aims to compare the performance of three different artificial neural network techniques for tourist demand forecasting: a multi-layer perceptron, a radial basis function and an Elman network. We find that multi-layer perceptron and radial basis function models outperform Elman networks. We repeated the experiment assuming different topologies regarding the number of lags used for concatenation so as to evaluate the effect of the memory on the forecasting results. We find that for higher memories, the forecasting performance obtained for longer horizons improves, suggesting the importance of increasing the dimensionality for long-term forecasting.

/pdf/tourism-demand-forecasting-with-neural-network-models-5807p475oz.pdf

Tourism demand forecasting with neural network models: different ways of treating information.

Purpose – This study aims to apply a new forecasting approach to improve predictions in the hospitality industry. To do so, the authors developed a multivariate setting that allows the incorporation of the cross-correlations in the evolution of tourist arrivals from visitor markets to a specific destination in neural network models. Design/methodology/approach – This multiple-input-multiple-output approach allows the generation of predictions for all visitor markets simultaneously. Official data of tourist arrivals to Catalonia (Spain) from 2001 to 2012 were used to generate forecasts for one, three and six months ahead with three different networks. Findings – The study revealed that multivariate architectures that take into account the connections between different markets may improve the predictive performance of neural networks. Additionally, the authors developed a new forecasting accuracy measure and found that radial basis function networks outperform the rest of the models. Research limitations/im...

/pdf/a-new-forecasting-approach-for-the-hospitality-industry-2dq45bbzkt.pdf

Oscar Claveria

Papers

Forecasting tourism demand to Catalonia: Neural networks vs. time series models

Business and consumer expectations and macroeconomic forecasts

Tourism demand forecasting with neural network models: different ways of treating information.

A new forecasting approach for the hospitality industry

Natural resources and human development: Evidence from mineral-dependent African countries using exploratory graphical analysis