COMPARING PERFORMANCE RESULTS USING NEWFM AND STATISTICAL METHOD

Sang-Hong Lee, Dong-Kun Shin, Joon S. Lim

2009

Abstract

This paper proposes stock forecasting using a principal component analysis (PCA) and a non-overlap area distribution measurement method based on a neural network with weighted fuzzy membership functions (NEWFM). The non-overlap area distribution measurement method selects the minimum number of four input features with the highest performance result from 12 initial input features by removing the worst input features one by one. PCA is a vector space transform often used for reducing multidimensional data sets to lower dimensions for analysis. The seven dimensional data sets with the highest performance result are extracted by PCA. The highest performance results in a non-overlap area distribution measurement method and PCA are 58.35% as the same results.

Paper Citation

in Harvard Style

Lee S., Shin D. and Lim J. (2009). COMPARING PERFORMANCE RESULTS USING NEWFM AND STATISTICAL METHOD . In Proceedings of the 4th International Conference on Software and Data Technologies - Volume 2: ICSOFT, ISBN 978-989-674-010-8, pages 353-356. DOI: 10.5220/0002252103530356

in Bibtex Style

@conference{icsoft09,
author={Sang-Hong Lee and Dong-Kun Shin and Joon S. Lim},
title={COMPARING PERFORMANCE RESULTS USING NEWFM AND STATISTICAL METHOD},
booktitle={Proceedings of the 4th International Conference on Software and Data Technologies - Volume 2: ICSOFT,},
year={2009},
pages={353-356},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002252103530356},
isbn={978-989-674-010-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Software and Data Technologies - Volume 2: ICSOFT,
TI - COMPARING PERFORMANCE RESULTS USING NEWFM AND STATISTICAL METHOD
SN - 978-989-674-010-8
AU - Lee S.
AU - Shin D.
AU - Lim J.
PY - 2009
SP - 353
EP - 356
DO - 10.5220/0002252103530356