A comparative study of Reservoir Computing strategies for monthly time series prediction

Authors:
F. Wyffels;B. Schrauwen
Affiliations:
Ghent University, Electronics and Information Systems Department, Sint-Pietersnieuwstraat 41, B-9000 Ghent, Belgium;Ghent University, Electronics and Information Systems Department, Sint-Pietersnieuwstraat 41, B-9000 Ghent, Belgium
Venue:
Neurocomputing
Year:
2010

Citing 16
Cited 4

Ridge regression: biased estimation for nonorthogonal problems

Technometrics
Time Series Analysis, Forecasting and Control

Time Series Analysis, Forecasting and Control
Real-time computing without stable states: a new framework for neural computation based on perturbations

Neural Computation
Predicting Time Series with Support Vector Machines

ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
2007 Special Issue: Edge of chaos and prediction of computational performance for neural circuit models

Neural Networks
2007 Special Issue: Optimization and applications of echo state networks with leaky- integrator neurons

Neural Networks
2007 Special Issue: An experimental unification of reservoir computing methods

Neural Networks
2007 Special Issue: Automatic speech recognition using a predictive echo state network classifier

Neural Networks
Editorial: Time series prediction competition: The CATS benchmark

Neurocomputing
Time series prediction with recurrent neural networks trained by a hybrid PSO-EA algorithm

Neurocomputing
2008 Special Issue: Event detection and localization for small mobile robots using reservoir computing

Neural Networks
Stable Output Feedback in Reservoir Computing Using Ridge Regression

ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Pruning and regularization in reservoir computing

Neurocomputing
Isolated word recognition with the Liquid State Machine: a case study

Information Processing Letters - Special issue on applications of spiking neural networks
The introduction of time-scales in reservoir computing, applied to isolated digits recognition

ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks

IEEE Transactions on Neural Networks

Editorial: European Symposium on Times Series Prediction

Neurocomputing
Recurrent kernel machines: Computing with infinite echo state networks

Neural Computation
Prediction for noisy nonlinear time series by echo state network based on dual estimation

Neurocomputing
Towards a predictive cache replacement strategy for multimedia content

Journal of Network and Computer Applications

Quantified Score

Hi-index	0.01

Visualization

Abstract

A good prediction of the future enables companies and governments to plan their investments, production and other needs. The demand for good forecasting techniques motivates many researchers coming from a wide variety of fields to develop methods for time series prediction. Many of these techniques are very complex to apply and demand lots of computational effort to execute. As an answer to this, we propose the use of Reservoir Computing, a recently developed technique for efficient training of recurrent neural networks, for monthly time series prediction. We will explain how Reservoir Computing in its basic form can be applied to time series prediction. Additionally we will extend this approach with different Reservoir Computing strategies such as seasonal adjustment or a Reservoir Computing based voting collective approach. We will investigate the performance of all the proposed strategies and compare its prediction accuracy with the linear forecasting procedure build in the Census Bureau's X-12-ARIMA program and a Nonlinear Autoregressive model using Least-Squares Support Vector Machines.