Young Geun Kim

Young Geun Kim

Postdoctoral Researcher

Yonsei University

Biography

Hello! I’m a postdoctoral researcher in statistics at Yonsei University. I received my Ph.D. in Statistics from Sungkyunkwan University (SKKU) in 2025. My thesis was Bayesian Modeling and Forecasting of High Dimensional Long Range Dependent Time Series, advised by Changryong Baek. My research focuses on high-dimensional time series, Bayesian econometrics, and time series anomaly detection.

Interests
  • High-dimensional time series
  • Long-range dependency
  • Bayesian econometrics
  • Time series anomaly detection
  • and more 🤔
Education

  • Ph.D. in Statistics, 2025

    Sungkyunkwan University, Seoul, Korea

    Dissertation title: Bayesian Modeling and Forecasting of High Dimensional Long Range Dependent Time Series

  • BEc in Statistics, 2019

    Sungkyunkwan University, Seoul, Korea

Experience

 
 
 
 
 
Yonsei University
Postdoctoral researcher
Yonsei University
Mar 2025 – Feb 2026 Seoul, Korea

Recent Publications

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Bayesian Vector Heterogeneous Autoregressive Modeling
Journal of Statistical Computation and Simulation
PDF Cite Dataset Project DOI R Package Codes
Bayesian Vector Heterogeneous Autoregressive Modeling
Residual Size is Not Enough for Anomaly Detection: Improving Detection Performance using Residual Similarity in Multivariate Time Series
SAC 2022 - Virtual Event
PDF Cite Project DOI Data - SWaT SWaT - seq2seq Data - WADI Data - HAI
Residual Size is Not Enough for Anomaly Detection: Improving Detection Performance using Residual Similarity in Multivariate Time Series
Revitalizing Self-Organizing Map: Anomaly Detection using Forecasting Error Patterns
IFIP SEC 2021 - Oslo, Norway
PDF Cite Code Project Slides DOI Data - SWaT Data - HAI
Revitalizing Self-Organizing Map: Anomaly Detection using Forecasting Error Patterns
Contextual Anomaly Detection by Correlated Probability Distributions using Kullback-Leibler Divergence
5th Workshop on Mining and Learning from Time Series, held in conjunction with KDD'19 Aug 5, 2019 - Anchorage, Alaska, USA
PDF Cite Code Dataset Project
Contextual Anomaly Detection by Correlated Probability Distributions using Kullback-Leibler Divergence

Recent Projects

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Bayesian Approaches to High-Dimensional Long-Range-Dependent Time Series
Forecast high-dimensional long-memory time series using Bayesian Vector Heterogeneous Autoregressive (BVHAR) model
Last updated on Jan 4, 2025 project
Code Dataset
Bayesian Approaches to High-Dimensional Long-Range-Dependent Time Series
Anomaly detection in Cyber-Physical Systems
Anomaly detection based on the deep neural network error.
Last updated on Oct 2, 2023 project
Data - SWaT Data - HAI R Python Team
Anomaly detection in Cyber-Physical Systems