Fuzzy time series and its models
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part I
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part II
Fuzzy Sets and Systems
Forecasting enrollments based on fuzzy time series
Fuzzy Sets and Systems
Incorporating managerial thinking in prediction and control: case study of market penetration
Journal of Optimization Theory and Applications
How to anticipate the Internet's global diffusion
Communications of the ACM
Investment using technical analysis and fuzzy logic
Fuzzy Sets and Systems - Special issue: Optimization and decision support systems
Expert Systems with Applications: An International Journal
Multivariate fuzzy forecasting based on fuzzy time series and automatic clustering techniques
Expert Systems with Applications: An International Journal
Measuring entrepreneurship: Expert-based vs. data-based methodologies
Expert Systems with Applications: An International Journal
A Critical Evaluation of Computational Methods of Forecasting Based on Fuzzy Time Series
International Journal of Decision Support System Technology
Hi-index | 12.06 |
The time-series models have been used to make reasonably accurate predictions in weather forecasting, academic enrolment, stock price, etc. This study proposes a novel method that incorporates trend-weighting into the fuzzy time-series models advanced by Chen's and Yu's method to explore the extent to which the innovation diffusion of ICT products could be adequately described by the proposed procedure. To verify the proposed procedure, the actual DSL (digital subscriber line) data in Taiwan is illustrated, and this study evaluates the accuracy of the proposed procedure by comparing with different innovation diffusion models: Bass model, Logistic model and Dynamic model. The results show that the proposed procedure surpasses the methods listed in terms of accuracy and SSE (Sum of Squares Error).