Nonlinear principal component analysis for withdrawal from the employment time guarantee fund

Abstract

To improve the management of the Employment Time Guarantee Fund (Fundo de Garantia do Tempo de Serviço - FGTS), a study in Brazil is conducted to analyze past data and anticipate the future trends of this fund. In this paper, Nonlinear Principal Component Analysis (NLPCA) - with the Artificial Neural Network architecture and Back-Propagation algorithm - is used to reduce the data dimension in describing various causes of withdrawals from the FGTS. With the analysis of the properties of these withdrawals, the paper discusses the correlation between the policy of free treatment of AIDS patients and their withdrawal from the plan. Nonlinear time series corresponding to each cause of withdrawal over 75 months - from 1994 to 2000 - are collected from the administrator of the FGTS. Using NLPCA, 17 small quantity time series (Group 1) are combined into one variable and then combined with other 7 middle quantity series (Group 2) to form another variable. Finally, four combined time series (Group 3) are formed which can well represent features of the total of 27 kinds of withdrawals with respect to their different causes. As a criterion for dimension reducing, the coefficient of correlation between the output of Group 1 and the sum of 17 is 0.8486 and that between Group 2 and sum of 8 is 0.9765. © 2007 Springer-Verlag Berlin Heidelberg.