5 papers
TEFL: Prediction-Residual-Guided Rolling Forecasting for Multi-Horizon Time Series
Xiannan Huang, Shen Fang, Shuhan Qiu +3
Time series forecasting plays a critical role in domains such as transportation, energy, and meteorology. Despite their success, modern deep forecasting models are typically traine…
Online time series prediction using feature adjustment
Xiannan Huang, Shuhan Qiu, Jiayuan Du +1
Time series forecasting is of significant importance across various domains. However, it faces significant challenges due to distribution shift. This issue becomes particularly pro…
Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Prediction
Xiannan Huang, Quan Yuan, Chao Yang
Accurately predicting short-term traffic demand is critical for intelligent transportation systems. While deep learning models achieve strong performance under stationary condition…
CONTINA: Confidence Interval for Traffic Demand Prediction with Coverage Guarantee
Chao Yang, Xiannan Huang, Shuhan Qiu +1
Accurate short-term traffic demand prediction is critical for the operation of traffic systems. Besides point estimation, the confidence interval of the prediction is also of great…
Predicting Subway Passenger Flows under Incident Situation with Causality
Xiannan Huang, Shuhan Qiu, Quan Yuan +1
In the context of rail transit operations, real-time passenger flow prediction is essential; however, most models primarily focus on normal conditions, with limited research addres…