1 citations · 2 across the 10 of their papers we have counts for
11 papers
LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence
Xingxuan Zhang, Gang Ren, Hao Yuan +57
We introduce LimiX-2, a new model in the LimiX family, developed through model and data scaling guided by our previously established scaling laws. LimiX-2 adopts the Contextual Mec…
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…
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…
Leveraging Intra-Period and Inter-Period Features for Enhanced Passenger Flow Prediction of Subway Stations
Xiannan Huang, Chao Yang, Quan Yuan
Accurate short-term passenger flow prediction of subway stations plays a vital role in enabling subway station personnel to proactively address changes in passenger volume. Despite…
Incorporating Long-term Data in Training Short-term Traffic Prediction Model
Xiannan Huang, Shuhan Qiu, Yan Cheng +2
Short-term traffic volume prediction is crucial for intelligent transportation system and there are many researches focusing on this field. However, most of these existing research…
Enhancing Traffic Prediction with Textual Data Using Large Language Models
Xiannan Huang
Traffic prediction is pivotal for rational transportation supply scheduling and allocation. Existing researches into short-term traffic prediction, however, face challenges in adeq…