Paper Info
Title
A Genetic Rough Set Approach to Fuzzy Time-Series Prediction
Abstract
Fuzzy Time series (FTS) has been widely applied to handle non-linear problems, such as enrollment estimation, weather prediction and stock index forecasting. FTS predicted values on the basis of an equal interval, which is determined the early stages of forecasting in the model. In this paper, we employed Genetic Algorithms (GA) to optimize the interval at first. Based on this, then Rough Set (RS) method is employed to recalculate the values. So the main purpose of this paper is to forecast a stock closing price by using the trend of the stock analyzed. We could identify trends of similar patterns from stock data to predict development of new data in the future. This proposed method is more efficient than the conventional FTS method.
Year
DOI
Venue
2016
10.1109/CMCSN.2016.40
2016 Third International Conference on Computing Measurement Control and Sensor Network (CMCSN)
Keywords
Field
DocType
Fuzzy time series,Genetic algorithm,Rough set,Forecasting,Stock Index
Data mining,Time series,Weather prediction,Computer science,Stock market index,Fuzzy logic,Rough set,Market research,Genetic algorithm
Conference
ISBN
Citations 
PageRank 
978-1-5090-1094-3
0
0.34
References 
Authors
4
3
Name
Order
Citations
PageRank
Junzo Watada141184.53
Jing Zhao210759.16
Yoshiyuki Matsumoto303.38