Temperature prediction and TAIFEX forecasting based on automatic clustering techniques and two-factors high-order fuzzy time series

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.