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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >A computational fuzzy time series forecasting model based on GEM-based discretization and hierarchical fuzzy logical rules
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A computational fuzzy time series forecasting model based on GEM-based discretization and hierarchical fuzzy logical rules

机译：基于基于离散元的离散化和层次模糊逻辑规则的计算模糊时间序列预测模型

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摘要

Fuzzy time series forecasting models have been widely used for predictions in various domains, including the prediction of stock prices, project costs, academic enrollment, electric load demand, etc. Current studies in this field mainly focus on three issues: the discretization of real numbers, the expression of evolutionary rules generated from training data and the defuzzification of the forecasted fuzzy results. To automatically and intelligently determine the discretization intervals, this paper introduces a general entropy measuring (GEM) method into the partitioning process of the original time series. A computational algorithm is also designed to realize the auto-determination process for each subset. Then, an improved hierarchical architecture is employed to express the fuzzy logical evolutionary rules of the fuzzy time series. To compare the performance of the proposed model with that of other models, the commonly used Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) dataset is used as experimental data. The forecasting results are evaluated using the root mean squared error (RMSE). The comparison results demonstrate the superiority of the proposed model. Finally, the Shanghai Stock Exchange Composite Index (SHSECI) stock price datasets from 1991 to 2014 are collected and used to test the model's applicability. The empirical results show that the proposed model can effectively handle large online datasets.
机译：模糊时间序列预测模型已广泛用于各个领域的预测，包括股票价格，项目成本，学术招生，电力负荷需求等的预测。该领域的当前研究主要集中在三个问题：实数离散化，从训练数据生成的进化规则的表达以及对预测的模糊结果进行模糊化处理。为了自动智能地确定离散间隔，本文将通用熵测量（GEM）方法引入原始时间序列的划分过程中。还设计了一种计算算法来实现每个子集的自动确定过程。然后，采用改进的层次结构来表达模糊时间序列的模糊逻辑进化规则。为了将建议的模型与其他模型的性能进行比较，将常用的台湾证券交易所资本化加权股票指数（TAIEX）数据集用作实验数据。使用均方根误差（RMSE）评估预测结果。比较结果证明了该模型的优越性。最后，收集了1991年至2014年的上海证券交易所综合指数（SHSECI）股票数据集，并用于检验该模型的适用性。实证结果表明，该模型可以有效处理大型在线数据集。

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