Department of Mathematics, Faculty of Applied Science, Thamar University, Dhamar 87246, Yemen; Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.
Department of Foundation and Applied Science, Universiti Teknologi PETRONAS, Seri Iskandar 32160, Malaysia.
Ecotoxicol Environ Saf. 2021 Dec 20;227:112875. doi: 10.1016/j.ecoenv.2021.112875. Epub 2021 Oct 28.
Fuzzy time series (FTS) forecasting models show a great performance in predicting time series, such as air pollution time series. However, they have caused major issues by utilizing random partitioning of the universe of discourse and ignoring repeated fuzzy sets. In this study, a novel hybrid forecasting model by integrating fuzzy time series to Markov chain and C-Means clustering techniques with an optimal number of clusters is presented. This hybridization contributes to generating effective lengths of intervals and thus, improving the model accuracy. The proposed model was verified and validated with real time series data sets, which are the benchmark data of actual trading of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and PM concentration data from Melaka, Malaysia. In addition, a comparison was made with some existing fuzzy time series models. Furthermore, the mean absolute percentage error, mean squared error and Theil's U statistic were calculated as evaluation criteria to illustrate the performance of the proposed model. The empirical analysis shows that the proposed model handles the time series data sets more efficiently and provides better overall forecasting results than existing FTS models. The results prove that the proposed model has greatly improved the prediction accuracy, for which it outperforms several fuzzy time series models. Therefore, it can be concluded that the proposed model is a better option for forecasting air pollution parameters and any kind of random parameters.
模糊时间序列 (FTS) 预测模型在预测时间序列方面表现出色，例如空气污染时间序列。然而，它们通过利用宇宙论的随机划分和忽略重复的模糊集而引起了重大问题。在本研究中，提出了一种新颖的混合预测模型，该模型将模糊时间序列与马尔可夫链和 C-Means 聚类技术集成，并采用最佳数量的聚类。这种混合有助于生成有效的间隔长度，从而提高模型的准确性。该模型使用实际的时间序列数据集进行了验证和验证，这些数据集是台湾证券交易所市值加权股价指数 (TAIEX) 的实际交易基准数据和马来西亚马六甲的 PM 浓度数据。此外，还与一些现有的模糊时间序列模型进行了比较。此外，还计算了平均绝对百分比误差、均方误差和 Theil 的 U 统计量作为评估标准，以说明所提出模型的性能。实证分析表明，与现有的 FTS 模型相比，所提出的模型更有效地处理时间序列数据集，并提供更好的整体预测结果。结果证明，所提出的模型大大提高了预测精度，在这方面优于几个模糊时间序列模型。因此，可以得出结论，所提出的模型是预测空气污染参数和任何随机参数的更好选择。
