Unveiling the Flaws: A Critical Analysis of Initialization Effect on Time Series Anomaly Detection

TL;DR

This paper critically examines how initialization impacts time series anomaly detection models, revealing high sensitivity to hyperparameters that can distort performance claims and emphasizing the need for rigorous evaluation standards.

Contribution

It provides the first comprehensive analysis of initialization effects on TSAD models, highlighting their influence on performance variability and evaluation reliability.

Findings

TSAD models are highly sensitive to hyperparameters like window size and seed number.

Minor initialization changes can cause performance fluctuations exceeding claimed improvements.

Current evaluation practices may overestimate model effectiveness due to sensitivity issues.

Abstract

Deep learning for time-series anomaly detection (TSAD) has gained significant attention over the past decade. Despite the reported improvements in several papers, the practical application of these models remains limited. Recent studies have cast doubt on these models, attributing their results to flawed evaluation techniques. However, the impact of initialization has largely been overlooked. This paper provides a critical analysis of the initialization effects on TSAD model performance. Our extensive experiments reveal that TSAD models are highly sensitive to hyperparameters such as window size, seed number, and normalization. This sensitivity often leads to significant variability in performance, which can be exploited to artificially inflate the reported efficacy of these models. We demonstrate that even minor changes in initialization parameters can result in performance variations that overshadow the claimed improvements from novel model architectures. Our findings highlight the need for rigorous evaluation protocols and transparent reporting of preprocessing steps to ensure the reliability and fairness of anomaly detection methods. This paper calls for a more cautious interpretation of TSAD advancements and encourages the development of more robust and transparent evaluation practices to advance the field and its practical applications.