Tourism Demand Forecasting: A Decomposed Deep Learning Approach

A technical analysis approach to tourism demand forecasting

C. Petropoulos, K. Nikolopoulos*, A. Patelis and V. Assimakopoulos Forecasting Systems Unit, School of Electrical and Computer Engineering, National Technical University of Athens, 9, Iroon Polytechniou Str, 15773 Zografou Athens, Greece Lancaster Centre for Forecasting, Department of Management Science, Lancaster University Management School, Lancaster LA1 4YX, UK Secretary for the Information...

Analyzing and Forecasting Tourism Demand: A Rough Sets Approach

Forecasting Tourism Demand with Composite Search Index

Researchers have adopted online data such as search query volumes to forecast tourism demand for a destination, including tourist volumes and hotel occupancy. However, the massive yet highly correlated query data pose challenges when researchers attempt to include them in the forecasting model. We propose a framework and procedure for creating a composite search index adopted in a generalized d...

Bayesian Models for Tourism Demand Forecasting

This study extends the existing forecasting accuracy debate in the tourism literature by examining the forecasting performance of various vector autoregressive (VAR) models. In particular, this study seeks to ascertain whether the introduction of the Bayesian restrictions (priors) to the unrestricted VAR process would lead to an improvement in forecasting performance in terms of achieving a hig...

Tourism Demand Forecasting by Improved SVR Model

The inboard tourism demand forecasting is very important to the development of tourism industry. In this paper, the SVR model is adopted to forecast monthly inbound tourism demand of China. And the elitist Non-dominated Sorting Genetic Algorithm (NSGAII) is used to parameter optimization. The NSGAII algorithm can reduce complexity of the algorithm, keeps the diversity of population and increasi...