| Summary: | 博士 === 義守大學 === 工業管理學系 === 102 === Grey forecast can effectively deal with small size and uncertain data and provide precise forecast values. Currently, numerous researchers have developed various grey forecast models. Many grey forecasting models have been developed by scholars but two new research issues have emerged. For the former problem, there are rare grey forecast studies for forecasting the interval-value data. For the latter problem, only several grey forecast studies can yield interval-value forecasted results.
To aim at solving the two problems, this study attempts to develop an integrated grey interval-value forecast method for small size and uncertain time series data. This method combines grey interval numbers, traditional forecast methods, clustering methods (linear regression method), the GM (1, 1) model and the NGBM (1, 1) model to develop six grey interval forecast methods. The first four methods are designed for the former problem, and the last two is designed for the latter problem. The six grey forecast methods can provide decision analysts with precise forecast range which allows them to make a right decision.
In addition, several small size interval-value and single-value data will be employed to evaluate the forecast accuracy between the proposed method and the previous grey forecast methods. Finally, two real-world cases are adopted for validating the forecast accuracy and practicability of the proposed method.
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