A New Approach to Multiple Time Series Prediction Using MIMO Fuzzy Aggregation Models with Modular Neural Networks

We present a new approach to multiple time series (MTS) prediction using many-inputs many-outputs (MIMO) fuzzy aggregation models (FAM) with modular neural networks (MNNs). We propose different FAM to generate the forecast outcome of MTS. Several representative approaches to MIMO-FAM are considered. The first FAM is designed with the use of adaptive neuro-fuzzy inference systems (ANFIS) using subtractive clustering (referred to as ANFIS-SC) and fuzzy C-means (referred to as ANFIS-FCM). The second FAM is developed based on Type-1 fuzzy inference systems, while the third FAM exploits interval Type-2 fuzzy inference systems. We design different MNN architectures, and the learning is carried out using backpropagation algorithms. The MTS used in the experiments concerned publicly available data including the Mexican Stock Exchange, National Association of Securities Dealers Automated Quotation and Taiwan Stock Exchange time series. The FAM are compared on the basis of the prediction errors based on commonly used performance indexes, such as the mean absolute error, the mean square error and the root-mean-square error. Simulation results demonstrate the effectiveness of the proposed methods.