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Pollution forecasting is important to research, especially for hazardous particles like PM _2.5 . However, trends and seasonality that can hide in the dataset make the forecasting model’s performance unfavorable. Our research aims to design a forecasting model methodology that involves trend and seasonality analysis using the seasonal decomposition of time series by Loess (STL). We obtained the PM _2.5 concentration dataset from Kaggle. The next step is applying the STL to analyze trends and seasonal components in datasets. Then we compared three forecasting models: gated recurrent unit (GRU), long short-term memory (LSTM), and one-dimensional convolutional LSTM (1D Conv-LSTM). We use the mean absolute error (MAE) for model performance comparisons. The STL test results show a trend component in the dataset that can influence forecasting performances. GRU exhibits a better MAE than LSTM …