FEFM: Feature Extraction and Fusion Module for Enhanced Time Series Anomaly Detection

Abstract

Time series data are utilized across various fields, including finance, healthcare, and manufacturing, where system reliability is crucial. Accordingly, extensive research on time series anomaly detection has been conducted. However, this task presents significant challenges due to high dimensionality, temporal dependencies, and noise in data. These challenges highlight the importance of effective feature extraction to capture meaningful data representations. In this study, we propose a novel Feature Extraction and Fusion Module (FEFM) specifically designed to enhance anomaly detection performance by extracting and fusing dimensional and temporal features of data. FEFM consists of three parts: Multi-Dimensional Feature Extractor (MDFE), Temporal Feature Extractor (TFE), and Feature Fusion Layers (FFL). MDFE and TFE extract dimensionally-driven and temporally-driven features from complex time series data. FFL then fuses these features with the original input, enabling the model to comprehensively understand the complex patterns. We evaluate the effectiveness of our method using seven benchmark datasets and two evaluation strategies, comparing its performance with other state-of-the-art methods. Experimental results demonstrate that our method outperforms others on various datasets, especially in reducing false positives. These results indicate that our method effectively extracts and fuses meaningful features from data, improving the reliability of anomaly detection systems.