Name
Abstract | ||
|---|---|---|
In this paper, we apply the self-organizing multilayer perceptron (SOMLP) architecture proposed by Gas for temporal prediction. Our main idea is to divide a data series into several smaller sub-series which are treated as individual functions or signals. Then we can find the tendencies in detail and perform predictions based on the properties of these signals. By using the SOMLP, signals can be clustered and similar sub-series for the underlying prediction are located. The idea is tested by forecasting the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and results are presented. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICMLC.2014.7009673 | ICMLC |
Keywords | Field | DocType |
function approximation,data series,multilayer perceptron,somlp architecture,taiwan stock exchange capitalization weighted stock index,temporal prediction,taiex,self-organizing,data analysis,multilayer perceptrons,self-organizing multilayer perceptron architecture,prediction,self-organising feature maps,time series,indexes | Data mining,Pattern recognition,Stock market index,Computer science,Stock exchange,Multilayer perceptron,Artificial intelligence,Data series,Machine learning | Conference |
Volume | ISSN | Citations |
2 | 2160-133X | 1 |
PageRank | References | Authors |
0.37 | 2 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheng-Ru Wang | 1 | 2 | 0.73 |
| Shie-Jue Lee | 2 | 48 | 5.11 |