Forecasting the demand for tourism using combinations of forecasts by neural network-based interval grey prediction models

ABSTRACT

In contrast to point forecasting, interval forecasting provides the degree of variation associated with forecasts. Accurate forecasting can help governments formulate policies for tourism, but little attention has been paid to interval forecasting of tourism demand. This study contributes to apply neural networks to develop interval models for tourism demand forecasting. Since combined forecasts are likely to improve the accuracy of point forecasting, forecast combinations are used to construct the proposed models. Besides, grey prediction models without requiring that data follow any statistical assumption serve as constituent models. Empirical results show that the proposed models outperform other considered interval models.

KEYWORDS:

• Tourism demand
• interval forecasting
• forecast combination
• grey theory
• neural network
• artificial intelligence

Acknowledgements

This research is supported by the Ministry of Science and Technology, Taiwan, under grant MOST 108-2410-H-033 -038-MY2.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Ministry of Science and Technology, Taiwan: [Grant Number MOST 108-2410-H-033 -038-MY2].