📉Detect Anomalies in Time-Series Data
Stop alerting on every weekend dip and missing the real incidents — learn to separate point, contextual, and collective anomalies, match each to the right detector (Z-score, isolation forest, LSTM autoencoder), and design an SLI alerting rule that survives weekly seasonality plus a slow trend.
Phase 1The Three Anomaly Types You're Conflating
Separate the three anomaly types your alerts confuse daily
Your weekend dip is not an incident
6 minYour weekend dip is not an incident
One value that doesn't belong
6 minOne value that doesn't belong
The value is fine; the timing is wrong
7 minThe value is fine; the timing is wrong
Each value is normal; the sequence is the problem
7 minEach value is normal; the sequence is the problem
Phase 2Detectors in Practice: Z-Score to LSTM
Run isolation forest and LSTM autoencoder on real telemetry
The cheapest detector works perfectly — until your data has a trend
7 minThe cheapest detector works perfectly — until your data has a trend
Build trees that isolate outliers in three splits
7 minBuild trees that isolate outliers in three splits
Teach a network what normal looks like, then alert on surprise
8 minTeach a network what normal looks like, then alert on surprise
Match the detector to the failure mode
6 minMatch the detector to the failure mode
Run all three on the same telemetry and watch them disagree
7 minRun all three on the same telemetry and watch them disagree
Phase 3Why Static Thresholds Fail on Seasonal Data
See why static thresholds fail on seasonal SLIs
Break the signal into trend + season + residual
7 minBreak the signal into trend + season + residual
The threshold that fired 400 times this month
7 minThe threshold that fired 400 times this month
Alert on what's left over, not what's expected
7 minAlert on what's left over, not what's expected
Slow drift hidden inside weekly waves
7 minSlow drift hidden inside weekly waves
Phase 4Design an Alerting Rule That Survives Seasonality
Design an alerting rule that survives weekly seasonality
Design an alerting rule for an SLI with weekly seasonality and a slow trend
8 minDesign an alerting rule for an SLI with weekly seasonality and a slow trend
Frequently asked questions
- What is anomaly detection in time-series data?
- This is covered in the “Detect Anomalies in Time-Series Data” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- What's the difference between point, contextual, and collective anomalies?
- This is covered in the “Detect Anomalies in Time-Series Data” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- When should I use isolation forest vs LSTM autoencoder for anomaly detection?
- This is covered in the “Detect Anomalies in Time-Series Data” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- Why do static thresholds fail on seasonal metrics?
- This is covered in the “Detect Anomalies in Time-Series Data” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- How do I write an alerting rule that doesn't fire on every weekend dip?
- This is covered in the “Detect Anomalies in Time-Series Data” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
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