2020-12-02T19:35:44Zhttps://gredos.usal.es/oai/requestoai:gredos.usal.es:10366/1344572020-09-24T07:44:50Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134243
Fyfe, Colin
Han, Ying
Corchado RodrÃguez, Emilio Santiago
2017-09-05T11:02:23Z
2017-09-05T11:02:23Z
2004
Neurocomputing. Volumen 57, pp. 37-47. Elsevier BV.
0925-2312 (Print)
http://hdl.handle.net/10366/134457
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.
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Elsevier BV
Attribution-NonCommercial-NoDerivs 3.0 Unported
https://creativecommons.org/licenses/by-nc-nd/3.0/
info:eu-repo/semantics/openAccess
Computer Science
Forecasting using twinned principal curves and twinned self-organising maps
info:eu-repo/semantics/article