Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress · HackerPapers discussions

Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress — mentioned by bsoles

Sat, 02 Aug 2025 04:22:07 GMT

There are also extremely misleading research articles out there promising good results with deep networks in the area of anomaly detection, without adequate comparison with more classical techniques. This well-known critical paper shows examples of AI articles/techniques applied to popular datasets with good-looking results. But, it also demonstrates that, literally, a single line of MATLAB code can outperform some of these techniques: https://arxiv.org/pdf/2009.13807

Paper and discussion history. A mention is not an endorsement.

Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress — mentioned by beryilma

Thu, 15 Aug 2024 16:57:02 GMT

Obligatory link from a sceptic: https://arxiv.org/pdf/2009.13807 > However, they wanted to develop a technique that avoids fine-tuning, a process in which engineers retrain a general-purpose LLM on a small amount of task-specific data to make it an expert at one task. This method does not avoid fine tuning. It just offloads the task to somebody else (i.e., to the LLM). I'll buy the promise of the approach when the authors can show that they can vastly outperform an AR time series model or the simple techniques mentioned in the linked article.

Paper and discussion history. A mention is not an endorsement.

Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress — HN submission

Wed, 29 Nov 2023 20:06:51 GMT

Current Time Series Anomaly Detection Benchmarks Are Flawed

2 points; 5 discussion replies on HN. Paper and source history. A submission is not an endorsement.