Triangular fuzzy series forecasting based on grey model and neural network

Two models for the forecasting of the triangular fuzzy-number (TF) series are presented in this paper. One is the triangular fuzzy-number grey model (TFGM (1, 1)). In this model, the fundamental equation of GM (1, 1) has been improved to be available for the application on the TF series. However, TFGM (1, 1) can be only applicable to the forecasting of the TF series with weak fluctuation because the essence of GM (1, 1) is to match the raw series with an exponential-type curve. In order to make it applicable to the forecasting of the fluctuating TF series, the neural network model is introduced to amend TFGM (1, 1). In the process of amendment, the TF series has been transformed into three real number series in order to avoid the disorder of the relative positions of the three boundary points of the triangular fuzzy number. Then the other model, the neural network TFGM (1, 1) (NNTFGM (1, 1)), is presented. The prediction of Consumer Price Indexes and the power load of one district of China illustrate that for the fluctuating TF series, the forecast accuracy of NNTFGM (1, 1) is higher than that of TFGM (1, 1).