18 citations · 47 across the 14 of their papers we have counts for
6 papers · 1 filter
From Patterns to Predictions: A Shapelet-Based Framework for Directional Forecasting in Noisy Financial Markets
Juwon Kim, Hyunwook Lee, Hyotaek Jeon +2
Directional forecasting in financial markets requires both accuracy and interpretability. Before the advent of deep learning, interpretable approaches based on human-defined patter…
ST-LINK: Spatially-Aware Large Language Models for Spatio-Temporal Forecasting
Hyotaek Jeon, Hyunwook Lee, Juwon Kim +1
Traffic forecasting represents a crucial problem within intelligent transportation systems. In recent research, Large Language Models (LLMs) have emerged as a promising method, but…
TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts
Hyunwook Lee, Sungahn Ko
Accurate traffic forecasting is challenging due to the complex dependency on road networks, various types of roads, and the abrupt speed change due to the events. Recent works main…
TILDE-Q: A Transformation Invariant Loss Function for Time-Series Forecasting
Hyunwook Lee, Chunggi Lee, Hongkyu Lim +1
Time-series forecasting has gained increasing attention in the field of artificial intelligence due to its potential to address real-world problems across various domains, includin…
Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting
Hyunwook Lee, Seungmin Jin, Hyeshin Chu +2
Traffic forecasting is a challenging problem due to complex road networks and sudden speed changes caused by various events on roads. A number of models have been proposed to solve…
An Empirical Experiment on Deep Learning Models for Predicting Traffic Data
Hyunwook Lee, Cheonbok Park, Seungmin Jin +3
To tackle ever-increasing city traffic congestion problems, researchers have proposed deep learning models to aid decision-makers in the traffic control domain. Although the propos…