Title :
Rule selection, fuzzy logic and time series
Author :
Benachenhou, Dalila
Author_Institution :
Dept. of Math. & Stat., American Univ., Washington, DC, USA
Abstract :
It is difficult to extract trading rules for financial trading systems. One reason is that the data often leads to conflicting rules, i.e., rules that have the same left-hand side but different right hand side. In this paper, we explore ways to deal with the problem of inconsistent rules using US ten-years bond time series data. We explore Wang´s method, and identify a particular shortcoming. In addition, we explore the use of frequency, and the last-in-sequence methods in choosing among inconsistent rules. We conclude that frequency can be used to develop short-term systems, while last-in-sequence produces overly biased rules
Keywords :
data analysis; financial data processing; fuzzy logic; knowledge based systems; time series; uncertainty handling; US ten-years bond time series data; financial trading systems; fuzzy logic; inconsistent rules; last-in-sequence methods; rule selection; Automatic control; Clustering algorithms; Control systems; Data mining; Frequency; Fuzzy logic; Fuzzy systems; Inference algorithms; Input variables; Testing;
Conference_Titel :
Fuzzy Information Processing Society Biannual Conference, 1994. Industrial Fuzzy Control and Intelligent Systems Conference, and the NASA Joint Technology Workshop on Neural Networks and Fuzzy Logic,
Conference_Location :
San Antonio, TX
Print_ISBN :
0-7803-2125-1
DOI :
10.1109/IJCF.1994.375101