ChaMTeC: CHAnnel Mixing and TEmporal Convolution Network for Time-Series Anomaly Detection
Source abstract
Abstract
Time-series anomaly detection is a critical task in various domains, including industrial control systems, where the early detection of unusual patterns can prevent system failures and ensure operational reliability. This paper introduces ChaMTeC (CHAnnel Mixing and TEmporal Convolution Network), a novel deep learning framework designed for time-series anomaly detection. ChaMTeC integrates an inverted embedding strategy, multi-layer temporal encoding, and a Mean Squared Error (MSE)-based feedback mechanism with dynamic thresholding to enhance anomaly detection performance. The framework is particularly tailored for industrial environments, where anomalies are rare and often subtle, making detection challenging. We evaluate ChaMTeC on six publicly available datasets and a newly introduced dataset, WaterLog, which is specifically designed to reflect real-world industrial control system scenarios with reduced anomaly rates. The experimental results demonstrate that ChaMTeC outperforms state-of-the-art models, achieving superior performance in terms of F1-CPA (Coverage-based Point-Adjusted F1) scores. The WaterLog dataset, which has been made publicly available, provides a more realistic benchmark for evaluating anomaly detection systems in industrial settings, addressing the limitations of existing datasets that often contain frequent and densely packed anomalies. Our findings highlight the effectiveness of combining channel-mixing techniques with temporal convolutional networks and dynamic thresholding for detecting anomalies in complex industrial environments. The proposed framework offers a robust solution for real-time anomaly detection, contributing to the reliability and sustainability of critical infrastructure systems.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Anomaly Detection Techniques and Applications · Time Series Analysis and Forecasting · Seismology and Earthquake Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer and Information Science (IDA), Linköping University, 581 83 Linköping, Sweden
Software Engineering, Sakarya University, Sakarya 54050, Türkiye
Computer Engineering, Sakarya University, Sakarya 54050, Türkiye
References (33)
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