Hassen Oday A, Darwish Saad M, Abu Nur A, Abidin Zaheera Z
Ministry of Education, Wasit Education Directorate, Kut 52001, Iraq.
Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, 163 Horreya Avenue, El-Shatby, Alexandria 21526, Egypt.
Entropy (Basel). 2020 Sep 6;22(9):991. doi: 10.3390/e22090991.
Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. The use of technical analysis for financial forecasting has been successfully employed by many researchers. The existing qualitative based methods developed based on fuzzy reasoning techniques cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. Extended fuzzy sets (e.g., fuzzy probabilistic set) study the fuzziness of the membership grade to a concept. The cloud model, based on probability measure space, automatically produces random membership grades of a concept through a cloud generator. In this paper, a cloud model-based approach was proposed to confirm accurate stock based on Japanese candlestick. By incorporating probability statistics and fuzzy set theories, the cloud model can aid the required transformation between the qualitative concepts and quantitative data. The degree of certainty associated with candlestick patterns can be calculated through repeated assessments by employing the normal cloud model. The hybrid weighting method comprising the fuzzy time series, and Heikin-Ashi candlestick was employed for determining the weights of the indicators in the multi-criteria decision-making process. Fuzzy membership functions are constructed by the cloud model to deal effectively with uncertainty and vagueness of the stock historical data with the aim to predict the next open, high, low, and close prices for the stock. The experimental results prove the feasibility and high forecasting accuracy of the proposed model.
预测股票价格在制定交易策略或确定买卖股票的合适时机方面起着重要作用。许多研究人员已成功地将技术分析用于金融预测。基于模糊推理技术开发的现有定性方法无法全面描述数据，这在不确定数据预测中极大地限制了模糊时间序列的客观性。扩展模糊集（如模糊概率集）研究概念隶属度的模糊性。基于概率测度空间的云模型通过云发生器自动生成概念的随机隶属度。本文提出了一种基于云模型的方法，以基于日本蜡烛图确定准确的股票。通过结合概率统计和模糊集理论，云模型有助于定性概念和定量数据之间的所需转换。通过使用正态云模型进行重复评估，可以计算与蜡烛图模式相关的确定性程度。在多标准决策过程中，采用了由模糊时间序列和黑田东彦蜡烛图组成的混合加权方法来确定指标的权重。通过云模型构建模糊隶属函数，以有效处理股票历史数据的不确定性和模糊性，旨在预测股票的下一个开盘价、最高价、最低价和收盘价。实验结果证明了所提模型的可行性和高预测准确性。
