  The current state of machine learning scholarship in Timeseries Anomaly
Detection (TAD) is plagued by the persistent use of flawed evaluation metrics,
inconsistent benchmarking practices, and a lack of proper justification for the
choices made in novel deep learning-based model designs. Our paper presents a
critical analysis of the status quo in TAD, revealing the misleading track of
current research and highlighting problematic methods, and evaluation
practices. Our position advocates for a shift in focus from solely pursuing
novel model designs to improving benchmarking practices, creating non-trivial
datasets, and critically evaluating the utility of complex methods against
simpler baselines. Our findings demonstrate the need for rigorous evaluation
protocols, the creation of simple baselines, and the revelation that
state-of-the-art deep anomaly detection models effectively learn linear
mappings. These findings suggest the need for more exploration and development
of simple and interpretable TAD methods. The increment of model complexity in
the state-of-the-art deep-learning based models unfortunately offers very
little improvement. We offer insights and suggestions for the field to move
forward.
  Code: https://github.com/ssarfraz/QuoVadisTAD
