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Abstract
This study introduces an advanced hybrid model integrating High-Order Hesitant Fuzzy Time Series (HOHFTS) with a Multilayer Perceptron (MLP) to improve the accuracy of air pollutant concentration forecasting. The model utilizes Hesitant Fuzzy Sets to define fuzzy sets, mean aggregated membership values to handle hesitant fuzzy elements, and MLP neural networks to capture complex relationships. A distinguishing feature is the determination of the optimal fuzzy time series (FTS) order, set to 24 in this study, to effectively capture daily temporal dependencies in the hourly air pollutant data. A case study using Semarang City’s air pollution dataset demonstrates the model's effectiveness, with preprocessing addressing missing values and interval-based discretization. Performance evaluation with Mean Absolute Error (MAE) and Symmetric Mean Absolute Percentage Error (SMAPE) indicates outstanding accuracy, with SMAPE values below 10% for most pollutants. Further exploration of hyperparameter optimization and order determination is recommended to enhance generalization and computational efficiency.
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