Paper Info
Title
An Intelligent Weighted Fuzzy Time Series Model Based on a Sine-Cosine Adaptive Human Learning Optimization Algorithm and Its Application to Financial Markets Forecasting.
Abstract
Financial forecasting is an extremely challenging task given the complex, nonlinear nature of financial market systems. To overcome this challenge, we present an intelligent weighted fuzzy time series model for financial forecasting, which uses a sine-cosine adaptive human learning optimization (SCHLO) algorithm to search for the optimal parameters for forecasting. New weighted operators that consider frequency based chronological order and stock volume are analyzed, and SCHLO is integrated to determine the effective intervals and weighting factors. Furthermore, a novel short-term trend repair operation is developed to complement the final forecasting process. Finally, the proposed model is applied to four world major trading markets: the Dow Jones Index (DJI), the German Stock Index (DAX), the Japanese Stock Index (NIKKEI), and Taiwan Stock Index (TAIEX). Experimental results show that our model is consistently more accurate than the state-of-the-art baseline methods. The easy implementation and effective forecasting performance suggest our proposed model could be a favorable market application prospect.
Year
DOI
Venue
2017
10.1007/978-3-319-69179-4_42
ADVANCED DATA MINING AND APPLICATIONS, ADMA 2017
Keywords
Field
DocType
Weighted fuzzy time series,Human learning optimization algorithm,Financial markets forecasting
Time series,Data mining,Weighting,Nonlinear system,Trigonometric functions,Computer science,Stock market index,Fuzzy logic,Operator (computer programming),Artificial intelligence,Financial market,Machine learning
Conference
Volume
ISSN
Citations 
10604
0302-9743
1
PageRank 
References 
Authors
0.35
21
5
Name
Order
Citations
PageRank
Ruixin Yang120131.97
Mingyang Xu221.37
Junyi He320.70
Stephen Ranshous4142.69
Nagiza F. Samatova586174.04