Breaking the Time-Frequency Granularity Discrepancy in Time-Series Anomaly Detection

Youngeun Nam, Susik Yoon, Yooju Shin, Minyoung Bae, Hwanjun Song, Jae-Gil Lee, Byung Suk Lee

Keywords: frequency domain, granularity, outlier, representation

TL;DR: This paper proposes a time-series anomaly detection framework that combines the time and frequency domains without the time-frequency granularity discrepancy by using nested-sliding windows.

Abstract: In light of the remarkable advancements made in time-series anomaly detection (TSAD), recent emphasis has been placed on exploiting the frequency domain as well as the time domain to address the difficulties in precisely detecting *pattern-wise* anomalies. However, in terms of anomaly scores, the *window granularity* of the frequency domain is inherently distinct from the *data-point granularity* of the time domain. Owing to this discrepancy, the anomaly information in the frequency domain has not been utilized to its full potential for TSAD. In this paper, we propose a TSAD framework, ***Dual-TF***, that simultaneously uses both the time and frequency domains while breaking the *time-frequency granularity discrepancy*. To this end, our framework employs *nested-sliding windows*, with the outer and inner windows responsible for the time and frequency domains, respectively, and aligns the anomaly scores of the two domains. As a result of the high resolution of the aligned scores, the boundaries of pattern-based anomalies can be identified more precisely. In six benchmark datasets, our framework outperforms state-of-the-art methods by 12.0–147%, as demonstrated by experimental results.

Track: Web Mining and Content Analysis

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Submission Number: 1432