Accurately Forecasting Model for the Stochastic Volatility Data in Tourism Demand

Ya‐Ling Huang · Yen‐Hsien Lee

44 citations0/0 files storedModern Economy

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Grey System Theory Applications Forecasting Techniques and Applications Traffic Prediction and Management Techniques

Abstract

This study attempts to enhance the effectiveness of stochastic volatility data. This work presents an empirical case involving the forecasting of tourism demand to demonstrate the efficacy of the accuracy forecasting model. Work combining the grey forecasting model (GM) and Fourier residual modification model to refine the forecasting effectiveness for the stochastic volatility data, which can estimate fluctuations in historical time series. This study makes the following contributions: 1) combining the grey forecasting and Fourier residual modification models to refine the forecasting effectiveness for the stochastic volatility data, 2) providing an effective method for forecasting the number of international visitors to Taiwan, 3) improving the accuracy of short-term forecasting in cases involving sample data with significant fluctuations.

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References

1982Control problems of grey systems 2006Support vector regression with genetic algorithms in forecasting tourism demand 2003A comparison of three different approaches to tourist arrival forecasting 2005Forecasting international tourist flows to Macau 2003Applying the Grey prediction model to the global integrated circuit industry 2003Modelling and forecasting the demand for Hong Kong tourism 2003Univariate versus multivariate time series forecasting: an application to international tourism demand 2007Tourism forecasting: To combine or not to combine?

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2020Forecasting tourism demand using fractional grey prediction models with Fourier series 2021Forecasting the demand for tourism using combinations of forecasts by neural network-based interval grey prediction models 2014An Improvement the Accuracy of Grey Forecasting Model for Cargo Throughput in International Commercial Ports of Kaohsiung

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