PFformer: A Time-Series Forecasting Model for Short-Term Precipitation Forecasting

Recent years witnessed increasing average annual precipitation in northern Xinjiang, amplifying the region’s vulnerability to natural disasters due to its fragile ecology and limited ground infiltration capacity. Therefore, the study of precipitation forecast in northern Xinjiang area is of vital significance to prevent these disasters. This study aims to capture the internal feature dependencies of complex multivariate meteorological data, build a PFformer model based on deep learning by reducing the algorithm time complexity of the short-term precipitation forecast model, use piecewise attention to reduce the algorithm complexity, and enhance the point-by-point dot product attention mechanism in the self-attention model. Data from five meteorological stations in Urumqi, Karamay, Tacheng, Altay, and Hami were used for the experiment. Comparing PFformer with mainstream time series prediction models, performance evaluation using Mean Squared Error (MSE) and Mean Absolute Error (MAE) indicates superior accuracy. Compared with the benchmark model, PFformer’s MSE improved by 7.85% on average and MAE improved by 9.74% on average. It shows potential for improving the accuracy of short-term precipitation forecasting, offering valuable assistance in disaster mitigation and management.