Rainfall time series prediction based on the DWT-SVR- Prophet hybrid model

Abstract

The discrete wavelet method can be used to decompose rainfall time series into subseries of different frequencies. It would be worthwhile to investigate whether combining forecasting results from different frequency subseries could improve the accuracy of rainfall prediction. A novel DWT-SVR-Prophet (DSP) hybrid model for rainfall prediction is proposed in this paper. First, the rainfall time series is decomposed into high-frequency and low-frequency subseries using discrete wavelet transform (DWT). The SVR and Prophet models are then used to predict high-frequency and low-frequency subsequences, respectively. Finally, the predicted rainfall is determined by summing the predicted values of each subsequence. A case study in China is conducted from January 1, 2014, to June 30, 2016. The results show that the DSP model provides excellent prediction, with RMSE, MAE, R2 values of 6.17, 3.3, and 0.75, respectively. The DSP model yields higher prediction accuracy than the three baseline models considered, with the prediction accuracy ranking as follows: DSP > SSP > Prophet > SVR. In addition, the DSP model is quite stable, and can achieve good results when applied to rainfall data from various climate types, with RMSEs ranging from 1.24 to 7.31, MAEs ranging from 0.52 to 6.14 and R2 values ranging from 0.62 to 0.75. The proposed model may provide a noval approach for rainfall forecasting and is readily adaptable to other time series predictions.