10 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…
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…
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…
Feature Fitted Online Conformal Prediction for Deep Time Series Forecasting Model
Xiannan Huang, Shuhan Qiu
Time series forecasting is critical for many applications, where deep learning-based point prediction models have demonstrated strong performance. However, in practical scenarios,…
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…
Individual Bus Trip Chain Prediction and Pattern Identification Considering Similarities
Xiannan Huang, Yixin Chen, Quan Yuan +1
Predicting future bus trip chains for an existing user is of great significance for operators of public transit systems. Existing methods always treat this task as a time-series pr…