LPCVAE: A Conditional VAE with Long-Term Dependency and Probabilistic Time-Frequency Fusion for Time Series Anomaly Detection

2025-10-12

AI Summary

Existing VAE-based time-series anomaly detection methods suffer from three key limitations: reliance on single-window modeling, insufficient capture of long-term temporal dependencies, and inadequate fusion of time-frequency information. To address these issues, this paper proposes a conditional variational autoencoder (CVAE) framework. The framework incorporates LSTM networks to model cross-window long-term dependencies, employs short-time Fourier transform (STFT) to extract time-frequency features, and introduces a distribution-level adaptive fusion mechanism based on Product of Experts (PoE) to enable probabilistic collaborative modeling of time-domain and frequency-domain latent representations. Extensive experiments on four public benchmark datasets demonstrate that the proposed method consistently outperforms state-of-the-art baselines. Ablation studies further confirm that both long-term dependency modeling and adaptive time-frequency fusion are critical for enhancing detection accuracy and robustness.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Computer Vision: Multi-modal Vision

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Time series anomaly detection(TSAD) is a critical task in signal processing field, ensuring the reliability of complex systems. Reconstruction-based methods dominate in TSAD. Among these methods, VAE-based methods have achieved promising results. Existing VAE-based methods suffer from the limitation of single-window feature and insufficient leveraging of long-term time and frequency information. We propose a Conditional Variational AutoEncoder with Long-term dependency and Probabilistic time-frequency fusion, named LPCVAE. LPCVAE introduces LSTM to capture long-term dependencies beyond windows. It further incorporates a Product-of-Experts (PoE) mechanism for adaptive and distribution-level probabilistic fusion. This design effectively mitigates time-frequency information loss. Extensive experiments on four public datasets demonstrate it outperforms state-of-the-art methods. The results confirm that integrating long-term time and frequency representations with adaptive fusion yields a robust and efficient solution for TSAD.

Problem

• Addresses single-window feature limitations in time series anomaly detection
• Solves insufficient leveraging of long-term temporal and frequency information
• Mitigates time-frequency information loss in reconstruction-based VAE methods

Innovation

• LSTM captures long-term dependencies beyond windows
• Product-of-Experts enables probabilistic time-frequency fusion
• Adaptive fusion integrates time-frequency representations efficiently