2020-12-04T03:09:13Zhttps://gredos.usal.es/oai/requestoai:gredos.usal.es:10366/1344572020-09-24T07:44:50Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134243
Forecasting using twinned principal curves and twinned self-organising maps
Fyfe, Colin
Han, Ying
Corchado RodrÃguez, Emilio Santiago
Computer Science
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.
2017-09-05T11:02:23Z
2017-09-05T11:02:23Z
2004
info:eu-repo/semantics/article
Neurocomputing. Volumen 57, pp. 37-47. Elsevier BV.
0925-2312 (Print)
http://hdl.handle.net/10366/134457
en
https://creativecommons.org/licenses/by-nc-nd/3.0/
info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivs 3.0 Unported
Elsevier BV