ForeNet : fourier recurrent networks for time series prediction
Yingqian Zhang, Laiwan Chan · TU/e Research Portal · 2000
Recurrent neural networks have been established as a general tool for tting sequential input=output data. On the other hand, Fourier analysis is a useful tool for time series analysis. In this paper, these two elds are linked together to form a new interpretation to recurrent networks for time series prediction. Fourier analysis of a time series is applied to construct a complex-valued recurrent neural network. The proposed network is called Fourier Recurrent Network (ForeNet). We showed the proper parameter initialization and the learning algorithm for the complex weights in ForeNet. Experimental results show that ForeNet speeds up the learning, and the generalization performance is superior to traditional recurrent network. 1 Introduction Time Delayed Recurrent networks are essentially feedforward networks with the addition of feedback connections. Due to the existence of these recurrent links, the recurrent neural network models preserve information through time and are more pow...