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**Using genetic algorithms grey theory to forecast high technology industrial output.**
*(English)*
Zbl 1163.91536

Summary: This paper presents an improved method to forecast the output and trends of high technology industry in Taiwan. High technology industry plays a pivotal role in this country’s economy and the social change. The characteristics of high technology industry include rapidly progressive of production techniques, fluctuating market demand, high investment capital, etc. These factors directly affect the difficulty of forecasting trends in this industry. The goal of this study is to overcome these constraints and establish a high-precision forecasting model. To do so, this paper proposes the combined use of grey theory and genetic algorithms. The former is used to forecast the outputs of high tech industry and latter is used to estimate the parameters of a forecasting model based on the minimization of forecasting error. The study uses the example of Taiwan’s integrated circuit industry. Results are very encouraging as this forecasting model clearly helps obtaining precision outcomes. The authors discuss implications of these findings for theory and practice.

### MSC:

91B84 | Economic time series analysis |

91B74 | Economic models of real-world systems (e.g., electricity markets, etc.) |

68T05 | Learning and adaptive systems in artificial intelligence |

### Software:

Excel
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\textit{C.-H. Wang} and \textit{L.-C. Hsu}, Appl. Math. Comput. 195, No. 1, 256--263 (2008; Zbl 1163.91536)

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