Inference Windowing Strategy Significantly Impacts Reconstruction-Based Time Series Anomaly Detection
A new study on arXiv examines how inference window overlap affects reconstruction-based anomaly detection models for time series data. The research finds that overlapping windows consistently improve performance across multiple model types by up to 28% on average, and can change which methods rank best. The findings emphasize that inference procedures, not just model architecture, are critical for fair comparison and practical anomaly detection performance.
Researchers conducted a systematic study of reconstruction-based time series anomaly detection methods, focusing on a previously underexamined factor: whether subsequences are processed as disjoint or overlapping windows during inference. Using the curated TSB-AD benchmark and the full UCR archive, they evaluated multiple model architectures including PCA baselines, DLinear, AutoEncoders, TimesNet, and Transformer variants under a unified training and evaluation protocol. Results consistently showed that overlapping inference windows outperformed disjoint windows across all tested models, with average improvements up to 28%, and notably, this choice could alter the relative ranking of methods. The study also analyzed variability across datasets, random seeds, and hyperparameter configurations, revealing that reconstruction-based baselines remain competitive and practical approaches for univariate time series anomaly detection when properly evaluated.
What's missing
The paper does not discuss computational cost implications of overlapping versus disjoint inference windows, nor does it address how findings generalize to multivariate time series anomaly detection.
What different sources said
- arXiv cs.LGCenter
Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detection
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