A variable P value rolling Grey forecasting model for Taiwan semiconductor industry production

Abstract

The semiconductor industry plays an important role in Taiwan's economy. In this paper, we constructed a rolling Grey forecasting model (RGM) to predict Taiwan's annual semiconductor production. The univariate Grey forecasting model (GM) makes forecast of a time series of data without considering possible correlation with any leading indicators. Interestingly, within the RGM there is a constant, P value, which was customarily set to 0.5. We hypothesized that making the P value a variable of time could generate more accurate forecasts. It was expected that the annual semiconductor production in Taiwan should be closely tied with U.S. demand. Hence, we let the P value be determined by the yearly percent change in real gross domestic product (GDP) by U.S. manufacturing industry. This variable P value RGM generated better forecasts than the fixed P value RGM. Nevertheless, the yearly percent change in real GDP by U.S. manufacturing industry is reported after a year ends. It cannot serve as a leading indicator for the same year's U.S. demand. We found out that the correlation between the yearly survey of anticipated industrial production growth rates in Taiwan and the yearly percent changes in real GDP by U.S. manufacturing industry has a correlation coefficient of 0.96. Therefore, we used the former to determine the P value in the RGM, which generated very accurate forecasts.