Learning from Polar Representation: An Extreme-Adaptive Model for
Long-Term Time Series Forecasting
- URL: http://arxiv.org/abs/2312.08763v2
- Date: Sat, 16 Dec 2023 10:46:48 GMT
- Title: Learning from Polar Representation: An Extreme-Adaptive Model for
Long-Term Time Series Forecasting
- Authors: Yanhong Li and Jack Xu and David C. Anastasiu
- Abstract summary: We propose Distance-weighted Auto-regularized Neural network (DAN), a novel extreme-adaptive model for long-range forecasting of stremflow enhanced by polar representation learning.
On four real-life hydrologic streamflow datasets, we demonstrate that DAN significantly outperforms both state-of-the-art hydrologic time series prediction methods and general methods designed for long-term time series prediction.
- Score: 10.892801642895904
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the hydrology field, time series forecasting is crucial for efficient
water resource management, improving flood and drought control and increasing
the safety and quality of life for the general population. However, predicting
long-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 but
important extreme values. Existing approaches often struggle to tackle these
dual challenges simultaneously. In this paper, we specifically delve into these
issues and propose Distance-weighted Auto-regularized Neural network (DAN), a
novel extreme-adaptive model for long-range forecasting of stremflow enhanced
by polar representation learning. DAN utilizes a distance-weighted multi-loss
mechanism and stackable blocks to dynamically refine indicator sequences from
exogenous data, while also being able to handle uni-variate time-series by
employing Gaussian Mixture probability modeling to improve robustness to severe
events. We also introduce Kruskal-Wallis sampling and gate control vectors to
handle imbalanced extreme data. On four real-life hydrologic streamflow
datasets, we demonstrate that DAN significantly outperforms both
state-of-the-art hydrologic time series prediction methods and general methods
designed for long-term time series prediction.
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