Future of Time Series Anomaly Detection: From Historical Review to Unified Outlook
3 judges. Scored by all three judges. Only the day’s top stories get the claim check. How far stories are checked
This survey gives a unified overview of time series anomaly detection, covering recent architectures such as diffusion models, state space models, and LLM-based detectors that earlier reviews largely omitted.
Score
0.63total under rubric v2.0
- Significance0.63
- Reader interest0.68
- Audience breadth0.56
- Practical relevance0.66
- Substantiated0.73
- Technical depth0.82
- New information0.83
- Counts against: Promotional0.06
- Counts against: Overclaim0.31
Each judge’s answer per question (0–1), their mean and spread, and what the question adds to the score (weight × mean).
| Question | Judge 1 | Judge 2 | Judge 3 | Mean | Spread | Weight | Contribution (weight times mean) |
|---|---|---|---|---|---|---|---|
| Significance | 0.70 | 0.45 | 0.72 | 0.63 | 0.27 | +0.25 | +0.156 |
| Reader interest | 0.61 | 0.60 | 0.82 | 0.68 | 0.22 | +0.20 | +0.135 |
| Audience breadth | 0.57 | 0.50 | 0.62 | 0.56 | 0.12 | +0.10 | +0.056 |
| Practical relevance | 0.75 | 0.55 | 0.68 | 0.66 | 0.20 | +0.15 | +0.099 |
| Substantiated | 0.84 | 0.60 | 0.74 | 0.73 | 0.24 | +0.10 | +0.073 |
| Technical depth | 0.90 | 0.70 | 0.86 | 0.82 | 0.20 | +0.10 | +0.082 |
| New information | 0.93 | 0.60 | 0.96 | 0.83 | 0.36 | +0.10 | +0.083 |
| Promotional | 0.05 | 0.05 | 0.08 | 0.06 | 0.03 | −0.15 | −0.009 |
| Overclaim | 0.34 | 0.25 | 0.34 | 0.31 | 0.09 | −0.15 | −0.046 |
| Sum of contributions | +0.629 | ||||||
| Claim-check penalty | 0.000 | ||||||
| Total, as published | 0.629 | ||||||
Spread is the highest minus the lowest judge answer. marks a spread above 0.40: the judges disagree, and the story is marked contested.
Also covered by
No other coverage was clustered with this story.
Elsewhere
Elsewhere: HN —
Recorded for context only, checked 24 Oct 2026, 08:54 UTC. It played no part in the ranking.