collaborators

6 papers

cs.SI2026

Modeling ICD-10 Morbidity and Multidimensional Poverty as a Spatial Network: Evidence from Thailand

Pratana Kukieattikool, Kittiya Ku-kiattikun, Anukool Noymai +5

Health and poverty in Thailand exhibit pronounced geographic structuring, yet the extent to which they operate as interconnected regional systems remains insufficiently understood.…

cs.DB2026

LGTD: Local-Global Trend Decomposition for Season-Length-Free Time Series Analysis

Chotanansub Sophaken, Thanadej Rattanakornphan, Piyanon Charoenpoonpanich +2

Time series decomposition into trend, seasonal, and residual components is a fundamental primitive in data mining and analytics pipelines, underpinning anomaly detection, change-po…

cs.AI2025

Multi-Band Variable-Lag Granger Causality: A Unified Framework for Causal Time Series Inference across Frequencies

Chakattrai Sookkongwaree, Tattep Lakmuang, Chainarong Amornbunchornvej

Understanding causal relationships in time series is fundamental to many domains, including neuroscience, economics, and behavioral science. Granger causality is one of the well-kn…

q-bio.NC2025

Reduced Efficiency in the Attentional Network During Distractor Suppression in Mild Cognitive Impairment

Jatupong Oboun, Piyanon Charoenpoonpanich, Anna Raksapatcharawong +4

Mild Cognitive Impairment (MCI) is a critical transitional stage between normal cognitive aging and dementia, making its early detection essential. This study investigates the neur…

cs.LG2025

Inferring the Most Similar Variable-length Subsequences between Multidimensional Time Series

Thanadej Rattanakornphan, Piyanon Charoenpoonpanich, Chainarong Amornbunchornvej

Finding the most similar subsequences between two multidimensional time series has many applications: e.g. capturing dependency in stock market or discovering coordinated movement…

cs.LG2025

From Features to Graphs: Exploring Graph Structures and Pairwise Interactions via GNNs

Phaphontee Yamchote, Saw Nay Htet Win, Chainarong Amornbunchornvej +1

Feature interaction is crucial in predictive machine learning models, as it captures the relationships between features that influence model performance. In this work, we focus on…