## Time Series Prediction by Using a Connectionist Network with Internal Delay Lines (1994)

Venue: | Time Series Prediction |

Citations: | 62 - 4 self |

### BibTeX

@INPROCEEDINGS{Wan94timeseries,

author = {Eric Wan},

title = {Time Series Prediction by Using a Connectionist Network with Internal Delay Lines},

booktitle = {Time Series Prediction},

year = {1994},

pages = {195--217},

publisher = {Addison-Wesley}

}

### Years of Citing Articles

### OpenURL

### Abstract

A neural network architecture, which models synapses as Finite Impulse Response (FIR) linear filters, is discussed for use in time series prediction. Analysis and methodology are detailed in the context of the Santa Fe Institute Time Series Prediction Competition. Results of the competition show that the FIR network performed remarkably well on a chaotic laser intensity time series. 1 Introduction The goal of time series prediction or forecasting can be stated succinctly as follows: given a sequence y(1); y(2); : : : y(N) up to time N , find the continuation y(N + 1); y(N + 2)::: The series may arise from the sampling of a continuous time system, and be either stochastic or deterministic in origin. The standard prediction approach involves constructing an underlying model which gives rise to the observed sequence. In the oldest and most studied method, which dates back to Yule [1], a linear autoregression (AR) is fit to the data: y(k) = T X n=1 a(n)y(k \Gamma n) + e(k) = y(k) + ...