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Abstract: In this paper, we proposed a modified turbulent particle swarm optimization (named MTPSO) method for the temperature prediction and the Taiwan Futures Exchange (TAIFEX) forecasting, based on the two-factor fuzzy time series and particle swarm optimization. The MTPSO model can be dealt with two main factors easily and accurately, which are the lengths of intervals and the content of forecast rules. The experimental results of the temperature prediction and the TAIFEX forecasting show that the proposed model is better than any existing models and it can get better quality solutions based on the high-order fuzzy time series, respectively. [Copyright &y& Elsevier]

Abstract: Accurate and immediate forecast in the short product life cycle of semiconductor market is difficult, but important. This paper proposed a grey model with factor analysis techniques to deal with the multi-factor forecasting problems. In the grey modeling, the use of genetic algorithm has the ability to search global optimum solution to construct two improved multivariable grey forecasting models that are AGAGM(1,N) and GAGM(1,N). These two models are applied for forecasting Taiwanese integrated circuit output. The results of the factor analysis show that the major factors of Taiwan’s integrated circuit output comprise R&D intensity, foreign investment, index of industrial production, trade specialization coefficient, and intra-industry trade coefficient. The improved multivariable grey forecasting models are found to be feasible and effective. [Copyright &y& Elsevier]

Abstract: In our daily life, we often use some forecasting techniques to predict weather, temperature, stock, earthquake, economy, etc. Based on these forecasting results, we can prevent damages to occur or get benefits from the forecasting activities. In fact, an event in the real-world can be affected by many factors. The more the facts we consider, the higher the forecasting accuracy rate. Moreover the length of each interval in the universe of discourse also affects the forecasting results. In this paper, we present a new method to predict the temperature and the Taiwan Futures Exchange (TAIFEX), based on automatic clustering techniques and two-factors high-order fuzzy time series. The proposed method gets higher average forecasting accuracy rates than the existing methods. [Copyright &y& Elsevier]