A Multi-factor and High-order Stock Forecast Model Based on Type-2 FTS Using Cuckoo Search and Self-adaptive Harmony Search
With the development of the economy,more and more factors and high orders data have a great influence on stock fluctuations.The fuzzy time series (Type-l) model has been studied by many scholars in recent years.In this paper,an enhanced model on the study of stock forecasting is proposed;this model is a multi-factor and high-order time series forecast model.This novel model extends the Type-2 fuzzy time series model by integrating several other factors.We firstly employed the Cuckoo search algorithm instead of the conventional average method to partition the universe of discourse and then proposed a novel self-adaptive harmony search algorithm to optimize the high orders.Furthermore,the Shanghai Stock Exchange Composite Index is used as both training and test data to verify the better performance of the proposed method,and the experimental results show that the proposed method outperforms other baseline methods.
Type-2 fuzzy time series Cuckoo search harmony search high orders and multi factors stock forecast
Wenyu Zhang Shixiong Zhang Shuai Zhang NingNing Huang
School of Information Management & Engineering, Zhejiang University of Finance and Economics, 18 Xueyuan Street, Xiasha, Hangzhou, China 310018
国内会议
杭州
英文
23-45
2016-11-01（万方平台首次上网日期，不代表论文的发表时间）