In spite of the impressive diversity of multi-factors fuzzy time series models, there is still a burning need to develop models that can handle the uncertainty inherent in certain data with some certain methods and obtain high forecasting accuracy. This paper proposes a novel multi-granularity combined prediction model for multi-factors fuzzy time series forecasting. The proposed model utilizes the clustering algorithm to generate different lengths of intervals, and forecasts the fuzzy trend in different granular spaces. It calculates the final forecasted value by using the forecasted fuzzy trend in each granular space and the optimal weighting vector obtained by particle swarm techniques. The proposed model can transform the uncertain problem to a certain problem through the granular computing theory. The experiments show that the presented model can not only obtain higher forecasting accuracy than several existing methods to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the civilian unemployment rate, but also capture and interpret the fuzzy trend. (C) 2015 Elsevier B.V. All rights reserved.