Contextual and Seasonal LSTMs for Time Series Anomaly Detection

2026-02-10

AI Summary

This work addresses the limitations of existing methods in detecting subtle point anomalies and slowly evolving anomalies in univariate time series. To overcome these challenges, the authors propose the CS-LSTMs framework, which enhances sensitivity to minor anomalies by jointly modeling temporal and spectral features while integrating contextual dependencies and seasonal patterns. A novel noise decomposition strategy is introduced to improve the modeling of periodic structures and the precision of anomaly localization. Extensive experiments on multiple public benchmark datasets demonstrate that CS-LSTMs significantly outperforms state-of-the-art approaches in detection performance, confirming its effectiveness and practical utility.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Univariate time series (UTS), where each timestamp records a single variable, serve as crucial indicators in web systems and cloud servers. Anomaly detection in UTS plays an essential role in both data mining and system reliability management. However, existing reconstruction-based and prediction-based methods struggle to capture certain subtle anomalies, particularly small point anomalies and slowly rising anomalies. To address these challenges, we propose a novel prediction-based framework named Contextual and Seasonal LSTMs (CS-LSTMs). CS-LSTMs are built upon a noise decomposition strategy and jointly leverage contextual dependencies and seasonal patterns, thereby strengthening the detection of subtle anomalies. By integrating both time-domain and frequency-domain representations, CS-LSTMs achieve more accurate modeling of periodic trends and anomaly localization. Extensive evaluations on public benchmark datasets demonstrate that CS-LSTMs consistently outperform state-of-the-art methods, highlighting their effectiveness and practical value in robust time series anomaly detection.

Problem

• time series anomaly detection
• univariate time series
• subtle anomalies
• point anomalies
• slowly rising anomalies

Innovation

• CS-LSTMs
• noise decomposition
• contextual dependencies
• seasonal patterns
• time-frequency representation