2020-11-29T20:46:08Zhttps://gredos.usal.es/oai/requestoai:gredos.usal.es:10366/1344572020-09-24T07:44:50Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134243
00925njm 22002777a 4500
dc
Fyfe, Colin
author
Han, Ying
author
Corchado RodrÃguez, Emilio Santiago
author
2004
We extend the principal curves algorithm by creating twinned principal curves which extend through two related data sets simultaneously. The criteria for accepting a pair of data points as neighbours for any other pair of data points is that each of the relevant points must be close in the appropriate space. We illustrate the algorithm's predictive power on artificial data sets before using it to predict on a real financial time series. We compare the error from this twinning with that achieved by a related algorithm which twins self-organising maps.
Neurocomputing. Volumen 57, pp. 37-47. Elsevier BV.
0925-2312 (Print)
http://hdl.handle.net/10366/134457
Computer Science
Forecasting using twinned principal curves and twinned self-organising maps