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In the hydrology field, time series forecasting is crucial for efficientwater resource management, improving flood and drought control and increasingthe safety and quality of life for the general population. However, predictinglong-term streamflow is a complex task due to the presence of extreme events.It requires the capture of long-range dependencies and the modeling of rare butimportant extreme values. Existing approaches often struggle to tackle thesedual challenges simultaneously. In this paper, we specifically delve into theseissues and propose Distance-weighted Auto-regularized Neural network (DAN), anovel extreme-adaptive model for long-range forecasting of stremflow enhancedby polar representation learning. DAN utilizes a distance-weighted multi-lossmechanism and stackable blocks to dynamically refine indicator sequences fromexogenous data, while also being able to handle uni-variate time-series byemploying Gaussian Mixture probability modeling to improve robustness to severeevents. We also introduce Kruskal-Wallis sampling and gate control vectors tohandle imbalanced extreme data. On four real-life hydrologic streamflowdatasets, we demonstrate that DAN significantly outperforms bothstate-of-the-art hydrologic time series prediction methods and general methodsdesigned for long-term time series prediction.

Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting