Structure estimation of the hierarchical networks and its application to anomaly detection

Anomaly detection of financial market and cause analysis through hierarchical structure analysis of financial time series data

Abstract

Purpose:Apply the Stochastic Block Model to reveal the latent structure of networks. By stacking layers, blocks are treated as upper nodes, allowing for the analysis of more complex network structures. Validate using both artificial and real financial data to detect structural changes during normal and abnormal periods and analyze the causes of anomalies. Method Overview:Estimate adjacency matrices from variables and train using Markov Chain Monte Carlo methods. Use the Akaike Information Criterion to estimate the optimal structure. For financial data, apply the Dynamic Time Warping and generate adjacency matrices based on co-occurrence relationships to estimate the structure with this model. Do anomaly detection by comparing structures during abnormal and normal periods. Results:Confirmed the effectiveness of the model with artificial data. Detected structural changes in financial time series data and achieved anomaly detection. The detected abnormal periods coincided with financial crises, and considerations were made about their causes.