Long-Term Hydrologic Time Series Prediction with LSPM

Predicting multivariate time series has been a topic of interest among researchers for a long time, especially in hydrological prediction. Due to the presence of extreme events, hydrological prediction requires capturing long-range dependencies and modeling rare but significant extreme values. Accurate prediction of these dependencies is often accomplished using complex models, such as stacked RNNs or transformer-based models, which can be computationally expensive and challenging to train. In addition, existing studies have identified a strong correlation between streamflow and rainfall data. However, the use of additional input data in these studies has often been insufficient, resulting in predictions with low accuracy. In this paper, we address these issues and propose LSPM, a Long Short-term Polar-Learning time series forecasting Model. LSPM learns polar representations through a feature reuse method called EDDU (Encoder Double-Decoder Unit). EDDU creatively incorporates exogenous input to generate long-term predictions based on these learned representations. To maximize the use of indicator sequences from exogenous data, LSPM enhances short-term predictions by a carefully designed loss function and integrates them into the overall forecast, improving robustness to short-term severe events. Experiments on four real-life hydrologic streamflow datasets demonstrate that LSPM significantly outperforms both state-of-the-art hydrologic time series prediction methods and general methods designed for long-term time series prediction.