A Multiple Model Approach to Time-Series Prediction Using an Online Sequential Learning Algorithm
提出一种简单的在线序贯学习算法用于前馈神经网络，证明其收敛性，并通过多模型切换与调优方法提升预测性能，优于其他同类算法。
Time-series prediction is important in diverse fields. Traditionally, methods for time-series prediction were based on fixed linear models because of mathematical tractability. Researchers turned their attention to artificial neural networks due to their better approximation capability. In this paper, we use feedforward neural networks with a single hidden layer, and present a rather simple online sequential learning algorithm (OSLA) together with its proof. The convergence properties of this algorithm are those of the well-known recursive least squares algorithm. We demonstrate that the prediction performance is better than other OSLAs, and show that it is statistically different from them. In addition, we also present the multiple models, switching, and tuning methodology that enhances the prediction performance of the learning algorithm.