Time Series Clustering for Stock Exchange in Asean Based on Non-Hierarchical Methods

Introduction: This study explores the impact of global economic volatility, particularly influenced by the Russia-Ukraine and Israel-Palestine conflicts, on the ASEAN stock markets. The research aims to analyze stock price patterns and trends to support sustainable economic planning and improve market stability. Methods: The study employed non-hierarchical clustering techniques, including K-Means and K-Medoids, to analyze time series data from 18 ASEAN stocks over a 10-year period. Data preprocessing involved Min-Max normalization, and Principal Component Analysis (PCA) was utilized for dimensionality reduction. The clustering performance was evaluated using silhouette coefficients, and the Elbow Method determined the optimal number of clusters. Results: K-Means demonstrated superior clustering performance with a silhouette coefficient of 0.63362 compared to K-Medoids (0.37133). The K-Means method identified seven distinct clusters, effectively grouping stocks with similar temporal patterns. The results revealed significant trends in price stability and volatility across different sectors. Conclusions: The findings highlight the value of clustering techniques in understanding market dynamics and provide actionable insights for policymakers and investors. The study recommends the development of real-time market monitoring systems to mitigate price fluctuations and support sustainable economic growth in ASEAN. Future research could explore integrating machine learning models for enhanced market analysis