177 citations · 196 across the 6 of their papers we have counts for
6 papers
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…
A Visual Analytics System for Improving Attention-based Traffic Forecasting Models
Seungmin Jin, Hyunwook Lee, Cheonbok Park +4
With deep learning (DL) outperforming conventional methods for different tasks, much effort has been devoted to utilizing DL in various domains. Researchers and developers in the t…
A Graphical Workflow Exploration Environment For Visual Analytics
Chunggi Lee, Juyoung Oh, Seungmin Jin +2
Graphical history mechanisms have been widely utilized in many domains to support humans' limited working memory, error recovery, collaboration, and presentation in visual analysis…
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…
ST-GRAT: A Novel Spatio-temporal Graph Attention Network for Accurately Forecasting Dynamically Changing Road Speed
Cheonbok Park, Chunggi Lee, Hyojin Bahng +5
Predicting road traffic speed is a challenging task due to different types of roads, abrupt speed change and spatial dependencies between roads; it requires the modeling of dynamic…