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Project	Deployment	Preview	Comments	Updated (UTC)
signoz-web	Ready	Preview	Comment	Sep 15, 2025 6:34am

Claude finished @srikanthccv's task —— View job

Review Complete

• Read CONTRIBUTING.md for review guidelines
• Read the changed documentation file
• Verify technical accuracy of anomaly detection content
• Verify mathematical calculations in examples
• Check formatting, links, and code examples
• Provide inline comments for specific issues
• Post summary review

Summary

This PR significantly improves the anomaly detection documentation by replacing the previous high-level overview with detailed, worked examples for each seasonality type (Hourly, Daily, Weekly). The changes are well-structured and provide valuable practical guidance.

Technical Accuracy ✅

The anomaly detection approach described is sound and aligns with established statistical methods:

• Seasonal decomposition is a well-established technique for time series analysis
• Z-score based anomaly detection (3+ standard deviations) is the industry standard threshold
• Mathematical calculations in all three examples are correct
• Algorithm formula correctly implements: prediction = moving_avg(past_period) + avg(current_season) - mean(past_seasons)

Issues Found

I've added 7 inline comments addressing:

1. Minor typos ("Drop on interest" → "Drop of interest")
2. Empty parentheses in variance/standard deviation notation (lines 243-244)
3. Inconsistent result formatting across examples
4. Missing clarification on standard deviation calculation context
5. Missing newline at end of file (POSIX compliance)

Strengths

• Comprehensive examples with realistic scenarios (e-commerce, payment gateway, SaaS)
• Clear time window breakdowns in table format
• Step-by-step calculations that readers can follow
• Good progression from simple (hourly) to complex (weekly) patterns
• Helpful context about when to use each seasonality type

Recommendation

Approve after addressing the inline comments. The issues are minor formatting/clarity improvements that don't affect the technical correctness of the content.