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

1 code implementation • 14 Dec 2023 • Yanhong Li, Jack Xu, David C. Anastasiu

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.

Management Representation Learning +3

Fast and Interpretable Mortality Risk Scores for Critical Care Patients

1 code implementation • 21 Nov 2023 • Chloe Qinyu Zhu, Muhang Tian, Lesia Semenova, Jiachang Liu, Jack Xu, Joseph Scarpa, Cynthia Rudin

Both of these have disadvantages: black box models are unacceptable for use in hospitals, whereas manual creation of models (including hand-tuning of logistic regression parameters) relies on humans to perform high-dimensional constrained optimization, which leads to a loss in performance.

An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural Networks

1 code implementation • 29 Nov 2022 • Yanhong Li, Jack Xu, David C. Anastasiu

Forecasting time series with extreme events has been a challenging and prevalent research topic, especially when the time series data are affected by complicated uncertain factors, such as is the case in hydrologic prediction.

Time Series Time Series Prediction