Handling forecasting problems based on two-factors high-order fuzzy time series

In our daily life, people often use forecasting techniques to predict weather, economy, population growth, stock, etc. However, in the real world, an event can be affected by many factors. Therefore, if we consider more factors for prediction, then we can get better forecasting results. In recent years, many researchers used fuzzy time series to handle prediction problems. In this paper, we present a new method to predict temperature and the Taiwan Futures Exchange (TAIFEX), based on the two-factors high-order fuzzy time series. The proposed method constructs two-factors high-order fuzzy logical relationships based on the historical data to increase the forecasting accuracy rate. The proposed method gets a higher forecasting accuracy rate than the existing methods.