A novel forecasting method based on multi-order fuzzy time series and technical analysis
A novel forecasting method based on multi-order fuzzy time series and technical analysis
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一种基于多阶模糊时间序列和技术分析的新型预测方法
DOI：
10.1016/j.ins.2016.05.038
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发表时间：
2016-11
影响因子：
8.1
通讯作者：
Defu Zhang
中科院分区：
文献类型：
--
作者：
Furong Ye;Liming Zhang;Defu Zhang
金融市场系统是一个复杂的非线性动态系统，金融交易是现代经济环境中最常见的风险投资行为之一。利用传统的时间序列预测技术来揭示其内在规律是一个挑战。本文提出了一种基于多阶模糊时间序列、技术分析和遗传算法的预测方法。该算法采用一阶、二阶和三阶模糊时间序列，并采用遗传算法寻找较好的区域划分，以提高算法的性能。利用ROC、MACD、KDJ等技术指标构造多变量模糊时间序列，并采用指数平滑法消除噪声。除了均方根误差和均方误差，方向准确率（DAR）也被用于我们的实证研究。我们应用所提出的方法来预测五个著名的股票指数和新台币兑美元汇率。实验结果表明，我们提出的方法优于其他现有的基于模糊时间序列的模型。
Financial trading is one of the most common risk investment actions in the modern economic environment because financial market systems are complex non-linear dynamic systems. It is a challenge to develop the inherent rules using the traditional time series prediction technique. In this paper, we proposed a new forecasting method based on multi-order fuzzy time series, technical analysis, and a genetic algorithm. Multi-order fuzzy time series (first-order, second-order and third-order) are applied in the proposed algorithm, and to improve the performance, genetic algorithm is used to find a good domain partition. Technical analysis such as the Rate of Change (ROC), Moving Average Convergence/Divergence (MACD), and Stochastic Oscillator (KDJ) are introduced to construct multi-variable fuzzy time series, and exponential smoothing is used to eliminate noise in the time series. In addition to the root mean square error and mean square error, the directional accuracy rate (DAR) is also used in our empirical studies. We apply the proposed method to forecast five well-known stock indexes and the NTD/USD exchange rates. Experimental results demonstrate that our proposed method outperforms other existing models based on fuzzy time series.
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DOI：
10.1109/icsmc.2011.6084021
发表时间：
2011-11
期刊：
2011 IEEE International Conference on Systems, Man, and Cybernetics
影响因子：
--
作者：
Shyi-Ming Chen;Gandhi Maruli Tua Manalu;Shu-Chuan Shih;T. Sheu;Hsiang-Chuan Liu
通讯作者：
Shyi-Ming Chen;Gandhi Maruli Tua Manalu;Shu-Chuan Shih;T. Sheu;Hsiang-Chuan Liu
DOI：
10.1016/j.physa.2008.01.099
发表时间：
2008-05
影响因子：
3.3
作者：
T. Jilani;S. Burney
通讯作者：
T. Jilani;S. Burney
DOI：
10.1016/j.eswa.2009.02.085
发表时间：
2009-10
期刊：
Expert Syst. Appl.
影响因子：
--
作者：
Shyi-Ming Chen;Nai-Yi Wang;Jeng-Shyang Pan
通讯作者：
Shyi-Ming Chen;Nai-Yi Wang;Jeng-Shyang Pan
基于混合模型的股指预测
DOI：
10.1016/j.omega.2011.07.008
发表时间：
2012-12-01
影响因子：
6.9
作者：
Wang, Ju-Jie;Wang, Jian-Zhou;Guo, Shu-Po
通讯作者：
Guo, Shu-Po
影响因子：
3.9
作者：
SONG, Q;CHISSOM, BS
通讯作者：
CHISSOM, BS