Linked Life Data

10065834

Source:http://linkedlifedata.com/resource/pubmed/id/10065834

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5-6
pubmed:dateCreated
1999-5-7
pubmed:abstractText
We propose a prediction model called Rival Penalized Competitive Learning (RPCL) and Combined Linear Predictor method (CLP), which involves a set of local linear predictors such that a prediction is made by the combination of some activated predictors through a gating network (Xu et al., 1994). Furthermore, we present its improved variant named Adaptive RPCL-CLP that includes an adaptive learning mechanism as well as a data pre-and-post processing scheme. We compare them with some existing models by demonstrating their performance on two real-world financial time series--a China stock price and an exchange-rate series of US Dollar (USD) versus Deutschmark (DEM). Experiments have shown that Adaptive RPCL-CLP not only outperforms the other approaches with the smallest prediction error and training costs, but also brings in considerable high profits in the trading simulation of foreign exchange market.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
0129-0657
pubmed:author
pubmed:issnType
Print
pubmed:volume
8
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
517-34
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:articleTitle
Adaptive rival penalized competitive learning and combined linear predictor model for financial forecast and investment.
pubmed:affiliation
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't