Inference Windowing Strategy Significantly Impacts Reconstruction-Based Time Series Anomaly Detection
Researchers have found that using overlapping rather than disjoint inference windows during reconstruction-based time series anomaly detection consistently improves performance across multiple model architectures. The study evaluated models including PCA baselines, DLinear, AutoEncoders, TimesNet, and Transformer variants on the TSB-AD benchmark and UCR archive, using a unified evaluation protocol. The findings highlight that inference procedure choices—not just model architecture—significantly affect anomaly detection results and can even alter method rankings.
A new preprint from arXiv proposes a systematic investigation into how inference windowing strategies affect reconstruction-based time series anomaly detection in the univariate offline setting. The authors introduce a unified training, tuning, and multi-seed evaluation protocol applied to the curated TSB-AD benchmark, addressing longstanding issues of heterogeneous and underspecified evaluation practices in the field. Across all tested models—including PCA-based baselines, DLinear, AutoEncoders, TimesNet, and Transformer variants—overlapping inference windows yielded consistent performance improvements, with average relative gains of up to 28% over disjoint windows. Notably, the choice of inference stride was found to alter rankings between methods, meaning conclusions about which model is best can change depending on this often-overlooked implementation detail. The study also extended its evaluation to the full UCR archive using localization criteria aligned with sliding-window reconstruction. A key takeaway is that simpler reconstruction-based baselines achieve strong performance when evaluated under a rigorous protocol, positioning them as competitive alternatives to more complex architectures.
What's missing
The study is limited to the univariate offline setting, leaving open questions about whether overlapping inference provides similar benefits for multivariate or online/streaming anomaly detection scenarios. As a preprint, the work has not yet undergone formal peer review.
What different sources said
- arXiv cs.LGCenter
Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detection
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