Transformer-Based Water Quality Forecasting With Dual Patch and Trend Decomposition

In many fields, time series prediction is gaining more and more attention, e.g., air pollution, geological hazards, and network traffic prediction. Water quality prediction uses historical data to predict future water quality. However, it is difficult to learn a representation map from a time series that captures the trends and fluctuations to effectively remove noise from the time series data and investigate complex nonlinear relationships. To solve these problems, this work proposes a time series prediction model, called DPSGT for short, which integrates Dual Patch Savitsky–Golay filtering and Transformer. First, DPSGT adopts the SG filtering to decompose the time series data and reduce the noise interference to improve long–term prediction capabilities. Second, to tackle the limitation of temporal representation capability, DPSGT adopts dual patches to ravel temporal series into local and global patches, which can tackle local semantic information and enlarge the receptive field. Third, it utilizes a transformer mechanism to address the nonlinear problem of the water quality time series and improve the accuracy of the prediction. Two real-world datasets are utilized to evaluate the proposed DPSGT, and experiments prove that DPSGT improves root mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R² by 6%, 5%, 8%, and 7%, respectively, compared with other benchmark models.