论文标题
基于间隔索引编号和成员价值的一种新颖的模糊时间序列序列预测，并使用支持向量机器
A novel method of fuzzy time series forecasting based on interval index number and membership value using support vector machine
论文作者
论文摘要
模糊的时间序列预测方法在研究人员中非常受欢迎，可以预测未来的价值，因为它们不是基于传统时间序列预测方法的严格假设。研究人员提供了更重要的预测结果，因此优先考虑模糊时间序列的非传统方法。 There are generally, four factors that determine the performance of the forecasting method (1) number of intervals (NOIs) and length of intervals to partition universe of discourse (UOD) (2) fuzzification rules or feature representation of crisp time series (3) method of establishing fuzzy logic rule (FLRs) between input and target values (4) defuzzification rule to get crisp forecasted value.考虑到提高预测准确性的前两个因素，我们提出了一种新型的非传统方法模糊时间序列预测，其中间隔索引编号和成员资格值用作输入特征来预测未来值。我们提出了一个简单的四舍五入范围和合适的步长方法，以找到最佳的间隔数（NOI）和使用模糊的C均值聚类过程，以将UOD分为不等长度的间隔。我们实施支持向量机（SVM）来建立FLR。为了测试我们提出的方法，我们对五个广泛使用的实时序列进行了模拟研究，并将其与最近开发的模型进行比较。我们还通过使用多层感知器（MLP）而不是SVM检查了所提出的模型的性能。两种绩效指标RSME和SMAPE用于性能分析，并观察到提出的模型可以更好地预测准确性。
Fuzzy time series forecasting methods are very popular among researchers for predicting future values as they are not based on the strict assumptions of traditional time series forecasting methods. Non-stochastic methods of fuzzy time series forecasting are preferred by the researchers as they provide more significant forecasting results. There are generally, four factors that determine the performance of the forecasting method (1) number of intervals (NOIs) and length of intervals to partition universe of discourse (UOD) (2) fuzzification rules or feature representation of crisp time series (3) method of establishing fuzzy logic rule (FLRs) between input and target values (4) defuzzification rule to get crisp forecasted value. Considering the first two factors to improve the forecasting accuracy, we proposed a novel non-stochastic method fuzzy time series forecasting in which interval index number and membership value are used as input features to predict future value. We suggested a simple rounding-off range and suitable step size method to find the optimal number of intervals (NOIs) and used fuzzy c-means clustering process to divide UOD into intervals of unequal length. We implement support vector machine (SVM) to establish FLRs. To test our proposed method we conduct a simulated study on five widely used real time series and compare the performance with some recently developed models. We also examine the performance of the proposed model by using multi-layer perceptron (MLP) instead of SVM. Two performance measures RSME and SMAPE are used for performance analysis and observed better forecasting accuracy by the proposed model.
