Andong Hahoe Cultural Village Demand Forecasting Model by Using Bass Diffusion Model

Previous researches usually applied Bass diffusion model (BDM) in forecasting the new product in various areas. This is the first application of BDM to the new tourism product since the model had been developed by Frank M. Bass in 1969. On the other hand, Grey forecasting model able to deal with limited number of data. Both BDM and grey forecasting model have been used in various areas in the forecasting studies. Taking advantages of both models, the combination of both Bass and grey model, called grey Bass forecasting model is applied in the context of the new tourism product forecasting. The objective of this study is to forecast the new tourism product demand in Malaysia using the developed model. Yearly visitors from two ecotourism resorts in Pahang, Tanah Aina Fahad and Tanah Aina Farrah Soraya from 2014 until 2018 are used. The results show that both BDM and grey Bass forecasting model are suitable in forecasting the new tourism product. The authors also suggest other factors affecting the attendance of visitors to be included in further research to conclude which model perform better in the future.

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A Comparison of Stochastic Cellular Automata Diffusion with the Bass Diffusion Model

SSRN Electronic Journal ◽

10.2139/ssrn.1663544 ◽

2010 ◽

Cited By ~ 1

Author(s):

Gadi Fibich ◽

Ro'i Gibori ◽

Eitan Muller

Keyword(s):

Cellular Automata ◽

Diffusion Model ◽

Stochastic Cellular Automata ◽

Bass Diffusion ◽

Bass Diffusion Model

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Logistics demand forecasting model based on improved neural network algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189479 ◽

2020 ◽

pp. 1-11

Author(s):

Hongjiang Ma ◽

Xu Luo

Keyword(s):

Neural Network ◽

Influencing Factor ◽

Demand Forecasting ◽

Forecasting Model ◽

Logistics System ◽

Model Based ◽

Network Algorithm ◽

Neural Network Algorithm ◽

Demand Forecasting Model ◽

Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

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