1995
Cortez, Paulo; Rocha, Miguel; Machado, Jose; Neves, Jose
Neural network based time series forecasting system Proceedings Article
Em: pp. 2689-2693, IEEE, Piscataway, NJ, United States, Perth, Aust, 1995, (cited By 9; Conference of Proceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) ; Conference Date: 27 November 1995 Through 1 December 1995; Conference Code:44687).
Resumo | Links | BibTeX | Etiquetas: Logic programming; Mathematical models; PROLOG (programming language); Time series analysis, Neural networks, Time series forecasting system
@inproceedings{Cortez19952689,
title = {Neural network based time series forecasting system},
author = {Paulo Cortez and Miguel Rocha and Jose Machado and Jose Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029452555&partnerID=40&md5=547210bf629ba7964e810106e283214a},
year = {1995},
date = {1995-01-01},
journal = {IEEE International Conference on Neural Networks - Conference Proceedings},
volume = {5},
pages = {2689-2693},
publisher = {IEEE, Piscataway, NJ, United States},
address = {Perth, Aust},
abstract = {The Neural Network (NN) arena has suffered in the past years a remarkable development as one of the novel fields for Artificial Intelligence (AI). Time Series Analysis (TSA) based on models of the variability of observations by postulating trends and cyclic effects, with a view to understand the cause of variation and to improve forecasting, suggests the use of NNs that do something like, what is called in statistics, Principal Component Analysis (PCA). The purpose of this work is to present a logical based NN system, along with: (i) Time Series Forecasting (TSF), with its characteristics of strong noise component and non-linearity in data, showing itself as a field in which the use of NN's stuff is particularly advisable; (ii) PCA rules, organized in a default hierarchy as logical theories, competing with one another for the right to represent a particular situation or to predict its successors; i.e., assisting in the process of choosing the best network to forecast each series. Some trials will be conducted, and the basic performance measures used as baselines for comparison with other methods.},
note = {cited By 9; Conference of Proceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) ; Conference Date: 27 November 1995 Through 1 December 1995; Conference Code:44687},
keywords = {Logic programming; Mathematical models; PROLOG (programming language); Time series analysis, Neural networks, Time series forecasting system},
pubstate = {published},
tppubtype = {inproceedings}
}
The Neural Network (NN) arena has suffered in the past years a remarkable development as one of the novel fields for Artificial Intelligence (AI). Time Series Analysis (TSA) based on models of the variability of observations by postulating trends and cyclic effects, with a view to understand the cause of variation and to improve forecasting, suggests the use of NNs that do something like, what is called in statistics, Principal Component Analysis (PCA). The purpose of this work is to present a logical based NN system, along with: (i) Time Series Forecasting (TSF), with its characteristics of strong noise component and non-linearity in data, showing itself as a field in which the use of NN's stuff is particularly advisable; (ii) PCA rules, organized in a default hierarchy as logical theories, competing with one another for the right to represent a particular situation or to predict its successors; i.e., assisting in the process of choosing the best network to forecast each series. Some trials will be conducted, and the basic performance measures used as baselines for comparison with other methods.