# 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.

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## 162 Citations

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

- Computer Science
- 1993

Time series analysis using RBF networks with FIR/IIR synapses

- Computer Science, EngineeringNeurocomputing
- 1998

Efficient Hybrid Neural Network for Chaotic Time Series Prediction

- Computer ScienceICANN
- 2001

The proposed hybrid neural network is constructed by a traditional feed-forward network, which is learned by using the backpropagation and a local models, which are implemented as a time delay embedding.

Model identification of time-delay nonlinear system with FIR neural network

- EngineeringProceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693)
- 2003

The FIR neural network model and its temporal backpropagation algorithm are introduced in this paper and the results show its good characteristics.

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

- Computer Science2009 1st IEEE Symposium on Web Society
- 2009

A multilayer neural network constructed by a Monte Carlo based algorithm to forecast time series events with high level of generalization ability is obtained without sensible choice of external parameters.

A modified FIR network for time series prediction

- Computer ScienceProceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.
- 2002

A modified FIR (Finite Impulse Response) network model for improving the capability of time series prediction system and can avoid the over-training effect that is caused by unbalanced learning data is presented.

On the prediction of the stochastic behavior of time series by use of Neural Networks - performance analysis and results

- Computer ScienceData Communications and their Performance
- 1995

A procedure is presented that automatically adapts to a given reference source in the sense that a simulated traffic source should show the same stochastic behavior as a reference source.

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

- Computer Science
- 2002

It is shown that wavelet MLP network provides prediction performance comparable to the conventional MLP, and after the less important inputs are eliminated, the waveletMLP shows more consistent performance for different weight initialization in comparison to theventional MLP.

Time-series data prediction based on reconstruction of missing samples and selective ensembling of FIR neural networks

- Computer ScienceProceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.
- 2002

This paper considers the problem of time-series forecasting by a selective ensemble neural network when the input data are incomplete and shows that the prediction made by the proposed method is more accurate than those predicted by neural networks without a fill-in process or by a single fill- in process.

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

- Computer ScienceNeurocomputing
- 2002

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