# Time series prediction by using a connectionist network with internal delay lines

@inproceedings{Wan1993TimeSP, title={Time series prediction by using a connectionist network with internal delay lines}, author={Eric A. Wan}, year={1993} }

A neural network architecture, which models synapses as Finite Impulse Response (FIR) linear lters, 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.

#### Topics from this paper.

#### Citations

##### Publications citing this paper.

SHOWING 1-10 OF 149 CITATIONS

## Modeling Nonlinear Dynamics with Neural Networks: Examples in Time Series Prediction

VIEW 3 EXCERPTS

CITES BACKGROUND & METHODS

## A modified FIR network for time series prediction

VIEW 1 EXCERPT

CITES BACKGROUND

## Time series forecasting using multilayer neural network constructed by a Monte-Carlo based algorithm

VIEW 1 EXCERPT

CITES METHODS

## FORECASTING USING COMPUTATIONAL INTELLIGENCE METHODS

VIEW 1 EXCERPT

CITES BACKGROUND

## Wavelet Multi-Layer Perceptron Neural Network for Time-Series Prediction

VIEW 2 EXCERPTS

CITES METHODS

## Learning long-term dependencies by the selective addition of time-delayed connections to recurrent neural networks

VIEW 1 EXCERPT

CITES BACKGROUND

### FILTER CITATIONS BY YEAR

### CITATION STATISTICS

**15**Highly Influenced Citations

#### References

##### Publications referenced by this paper.

SHOWING 1-10 OF 35 REFERENCES

## Predicting the Future: a Connectionist Approach

VIEW 2 EXCERPTS

## Temporal backpropagation for FIR neural networks

VIEW 1 EXCERPT

## Generalization of backpropagation with application to a recurrent gas market model

VIEW 4 EXCERPTS

HIGHLY INFLUENTIAL

## Neural Networks and the Bias/Variance Dilemma

VIEW 1 EXCERPT

## Phoneme recognition using time-delay neural networks

VIEW 3 EXCERPTS