Forecasting innovation diffusion of products using trend-weighted fuzzy time-series model

Cheng, Ching‐Hsue; Chen, You-Shyang; Wu, Ya‐Ling

doi:10.1016/j.eswa.2007.12.041

Cited by 47 publications

(25 citation statements)

References 27 publications

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“…To find the correlation between stock volume and price in stock market, Chu et al [25] proposed a dual-factor method. Cheng et al [26] proposed another novel method incorporating trendweighting into the fuzzy time-series models of Chen and Yu and applied such a method to explore the extent to which the innovation diffusion of ICT products is described using this procedure. Wang and Chen [27] presented a new method to predict the temperature and TAIEX using automatic clustering and two-factor high-order fuzzy time series for temperature prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

An improved Fuzzy Time Series forecasting model based on Particle Swarm intervalization

Davari

Zarandi

Türkşen

2009

NAFIPS 2009 - 2009 Annual Meeting of the North American Fuzzy Information Processing Society

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The objective of this paper is to show the strength of a modified version of Particle Swarm Optimization (PSO) in definition of suitable partitions of fuzzy time series forecasting and increasing its accuracy. Although a lot of contributions have been made to increase the quality of forecasts using Fuzzy Time Series during recent years, there are only a few papers considering tuning the length of intervals in forecasting. In this paper, we propose a new method to tune the length of forecasting intervals and show the superiority of our procedure to those previously proposed using the well-known data of University of Alabama. The main contribution of this paper is to use a modified and effective PSO algorithm in which velocities are updated using a modified version of traditional PSO in order to have some diversification in solutions generated. In addition, monotonically decreasing functions for PSO parameters are used to improve the accuracy of forecast. The results show that our model outperforms other methods in the literature.

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Section: Literature Reviewmentioning

confidence: 99%

An improved Fuzzy Time Series forecasting model based on Particle Swarm intervalization

Davari

Zarandi

Türkşen

2009

NAFIPS 2009 - 2009 Annual Meeting of the North American Fuzzy Information Processing Society

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“…During the last ten years, a series of research results on combined forecasting models and methods have been acquired. For example, CHENG et al [6] proposed a novel method that incorporated trend-weighting into the fuzzy time-series models to explore the extent to which the innovation diffusion of information and communication technologies products could be adequately described by the proposed procedure. AGAMI et al [7] introduced a new approach for constructing trend impact analysis by using a dynamic forecasting model based on neural networks and enhancing the trend impact analysis prediction process.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear combined forecasting model based on fuzzy adaptive variable weight and its application

Jiang

Mei

Jiaqiang

et al. 2010

J. Cent. South Univ. Technol.

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In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system, a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error, the change tendency of the forecasted object, gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight. Based on Visual Basic 6.0 platform, a fuzzy adaptive variable weight combined forecasting and management system was developed. The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.

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“…Consequently, it is understandable that many studies choose to investigate the key factors that influence the acceptance or the rejection of an innovation during the process of diffusion. Based on these key factors including price and advertising, researchers have contributed to the development of the diffusion theory by suggesting analytical models for describing and forecasting the diffusion process of an innovation in a social system [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] such researchers include Fourt, Mansfield, and Mahajan. Fourt and Mansfield discussed the diffusion pattern in external influence such as mass advertisement or internal influence such as oral communication separately [8,12].…”

Section: Introductionmentioning

confidence: 99%

A Dynamical Innovation Diffusion Model with Fuzzy Coefficient and Its Application to Local Telephone Diffusion in China

Liao

Yao

2013

Discrete Dynamics in Nature and Society

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This paper studies the innovation diffusion problem with the affection of urbanization, proposing a dynamical innovation diffusion model with fuzzy coefficient, and uses the shifting rate of people from rural areas stepping into urban areas to show the process of urbanization. The numerical simulation shows the diffusion process for telephones in China with Genetic Algorithms and this model is effective to show the process of innovation diffusion with the condition of urbanization process.

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Forecasting innovation diffusion of products using trend-weighted fuzzy time-series model

Cited by 47 publications

References 27 publications

An improved Fuzzy Time Series forecasting model based on Particle Swarm intervalization

An improved Fuzzy Time Series forecasting model based on Particle Swarm intervalization

Nonlinear combined forecasting model based on fuzzy adaptive variable weight and its application

A Dynamical Innovation Diffusion Model with Fuzzy Coefficient and Its Application to Local Telephone Diffusion in China

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