Hybridization Model for Air Pollution Prediction Using Time Series Data - Roni Yunis; Andri Andri; Djoni Djoni (CogITo Smart Journal, 2024)
In recent years, data science analysis, particularly time series predictions, has been widely employed across various industrial sectors. However, time series data presents high complexity, especially in seasonal patterns such as monthly, daily, or hourly fluctuations. Irregular fluctuations and external factors increasingly challenge accurate predictions. Therefore, this research proposes a hybrid approach combining SVR-SARIMA, SVR-Prophet, LSTM-SARIMA, and LSTM-Prophet to enhance time series prediction accuracy. This study followed the OSEMN methodology approach: gathering data, cleaning data, exploring data, developing models, and interpreting crucial aspects of problem-solving. Seasonal effect predictions indicated a rise in SO2 and NO2 during dry and rainy seasons until the next two years (average daily increments of 0.0831 μg/m3 for SO2 and 0.0516 μg/m3 for NO2). Estimates suggest a decrease in the order of three particles. The evaluation showed that the SVR model performed better compared to the other three models (RMSE 7.765, MAE 5.477, and MAPE 0.261). The best-performing hybrid model was LSTM-Prophet (99.74% accuracy) with RMSE 12.319, MAE 12.057, and MAPE 0.259 values.