Neural Short-Term Prediction Based on Dynamics Reconstruction

Authors:
F. Camastra;A. M. Colla
Affiliations:
Research & Development Department, Elsag Bailey – Finmeccanica S.p.A., Via G. Puccini 2, 16154 Genova, Italy. E-mail: francesco.camastra@elsag.it;Research & Development Department, Elsag Bailey – Finmeccanica S.p.A., Via G. Puccini 2, 16154 Genova, Italy. E-mail: francesco.camastra@elsag.it
Venue:
Neural Processing Letters
Year:
1999

Citing 2
Cited 4

Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Time Series Analysis, Forecasting and Control

Time Series Analysis, Forecasting and Control

Unfolding preprocessing for meaningful time series clustering

Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Forecasting the CATS benchmark with the Double Vector Quantization method

Neurocomputing
Chaotic time series method combined with particle swarm optimization and trend adjustment for electricity demand forecasting

Expert Systems with Applications: An International Journal
Fuzzy neural very-short-term load forecasting based on chaotic dynamics reconstruction

ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present an application of dynamicsreconstruction techniques to model order estimation. Both theGrassberger–Procaccia and the Takens‘ methodwere applied, yielding similar values for the correlation dimension, hence for the model order. Based on this model order, appropriately structured neural nets for short-term prediction were designed. Satisfactory experimental results were obtained in one-hour-ahead electrical load forecasting ona six-month benchmark from an electric utility in the U.S.A.