Neural Network-Based Change Point Detection for Large-Scale Time-Evolving Data

2025-03-12

AI Summary

This study addresses the automatic detection and precise localization of change points in large-scale multivariate time series exhibiting dynamic evolution. We propose a two-stage change point detection method based on feedforward neural networks (FNNs). The method innovatively integrates FNNs into the change point detection framework, incorporating piecewise training, sliding-window error evaluation, and an online recalibration mechanism, along with a dedicated error calibration strategy to ensure estimation consistency under temporal dependence. Theoretically, we establish consistency of the change point localization estimator. Empirically, the method achieves high accuracy in estimating both the number and locations of change points on both synthetic and real-world datasets. Moreover, it supports practical, data-driven selection of hyperparameters, enhancing its applicability in real-world scenarios.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Natural Language Processing: Learning & Optimization for NLP
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

The paper studies the problem of detecting and locating change points in multivariate time-evolving data. The problem has a long history in statistics and signal processing and various algorithms have been developed primarily for simple parametric models. In this work, we focus on modeling the data through feed-forward neural networks and develop a detection strategy based on the following two-step procedure. In the first step, the neural network is trained over a prespecified window of the data, and its test error function is calibrated over another prespecified window. Then, the test error function is used over a moving window to identify the change point. Once a change point is detected, the procedure involving these two steps is repeated until all change points are identified. The proposed strategy yields consistent estimates for both the number and the locations of the change points under temporal dependence of the data-generating process. The effectiveness of the proposed strategy is illustrated on synthetic data sets that provide insights on how to select in practice tuning parameters of the algorithm and in real data sets. Finally, we note that although the detection strategy is general and can work with different neural network architectures, the theoretical guarantees provided are specific to feed-forward neural architectures.

Problem

• Detecting change points in multivariate time-evolving data
• Using neural networks for accurate change point identification
• Ensuring consistent estimates under temporal data dependence

Innovation

• Feed-forward neural networks model data.
• Two-step procedure detects change points.
• Moving window identifies temporal changes.