Speaker
Description
Outlier detection in functional time series is challenging due to temporal dependence and the
coexistence of magnitude, shape, and partially contaminated anomalies. Existing methods often assume independence or rely on model-based approaches, such as the Standard Smoothed Bootstrap
on Residuals (SmBoR), which may perform poorly under model misspecification. Model-free alternatives, such as the Moving Block Bootstrap (MBBo), improve robustness but may show modest
true positive rates for magnitude anomalies. This work proposes a fully model free pipeline with two
components. First, the Directional Outlyingness (DirOut) framework is extended by recalibrating
its cutoff via MBBo, improving detection of shape and partial outliers while controlling false positives.
Second, a Sliding Window Functional Boxplot (SWOD) is introduced to exploit local temporal
neighborhoods and detect magnitude anomalies that global summaries may miss. Simulations show
that SWOD achieves high detection rates for magnitude outliers, while MBBo calibrated DirOut
attains near perfect detection for shape and partial anomalies, outperforming SmBoR. The approach
is further validated on a real temperature dataset, demonstrating its practical effectiveness.
53573503884