dt-obs-predictive-analytics

Predictive Analytics Skill

Forecast resource saturation, detect trends, analyze anomalies, and characterize signal behavior using DQL and Dynatrace analyzer tools.

Analysis Disciplines

Choosing the Right Detection Tool

The single most important decision: are you asking "did this metric change?" or "is this metric currently wrong?"

Decision rule in plain language

• Use timeseries-novelty-detection when the question contains "changed", "shifted", "spiked", "dropped", "started", "when did", or "did anything unusual happen". The tool answers whether a change occurred and when. It requires no predefined threshold.
• Use an anomaly detector (adaptive, seasonal, or static) when the question is about ongoing or current state relative to an expected range: "which are highest", "who is violating", "what is above X". These tools count violation samples inside a sliding window — they confirm how long something has been bad, not whether the signal changed.

Pitfall: Running adaptive-anomaly-detector on a broad fleet to answer "which service changed load?" typically flags every service that has any variation, producing low-signal results. Use timeseries-novelty-detection first to identify entities where the load character genuinely shifted, then use the anomaly detectors to measure the severity of those specific signals.

When to Use This Skill

• Capacity: "Which hosts will hit 90% CPU in the next 30 days?"
• Forecast: "Forecast service request volume for the next 7 days"
• Trend: "Is memory usage growing across our Kubernetes nodes?"
• Anomaly: "Which services have unusual error rates right now?"
• Baseline: "How does today's traffic compare to last week?"
• Signal profile: "Is this metric seasonal or trending before I set up alerting?"

Important Constraints

Dynatrace Forecast Analyzer supports univariate forecasting only — predicting one metric based on its own historical values. Multivariate forecasting (using multiple metrics as inputs) requires external tools (Python, R, Azure AutoML).

Tooling Rule: Run analyses using Dynatrace tools: timeseries-forecast, adaptive-anomaly-detector, seasonal-baseline-anomaly-detector, static-threshold-analyzer, and timeseries-novelty-detection. Use execute-dql for DQL queries.

Result Analysis Rule: Always analyse and summarise results directly from the raw tool output. Derive all numbers, trends, and conclusions inline.

Result Presentation Format

Always present forecast results as a structured table:

Always follow the table with a Key Findings section (3–5 bullet points, ranked by priority).

Core DQL Techniques

DQL has no native forecast function. For forward-looking forecasts, use timeseries-forecast (see references/forecasting-analyzer.md).

Key DQL Rules

1. timeseries returns arrays — one value per time slot per entity
2. arrayLast(arr) = most recent value; arrayFirst(arr) = oldest
3. Growth = (arrayLast - arrayFirst) / number_of_intervals
4. Always filter isNotNull(field) before sorting to avoid null ordering issues
5. Use toLong() when dividing Long fields to avoid type errors
6. Use dt.smartscape.* not deprecated dt.entity.* in DQL display fields; use dt.smartscape.* in by:{} grouping clauses for entity-level queries

Standard Query Patterns

Moving Average Trend

timeseries cpu = avg(dt.host.cpu.usage), from: now()-24h, interval: 1h, by: {dt.smartscape.host}
| fieldsAdd moving_avg = arrayMovingAvg(cpu, 4)
| fieldsAdd current = arrayLast(cpu)
| fieldsAdd trend = arrayLast(cpu) - arrayFirst(cpu)
| filter isNotNull(current)
| sort trend desc
| limit 20
| fields dt.smartscape.host, current, trend, moving_avg

Saturation Risk Classification

timeseries cpu = avg(dt.host.cpu.usage), from: now()-7d, interval: 1h, by: {dt.smartscape.host}
| fieldsAdd p95 = arrayPercentile(cpu, 95)
| fieldsAdd saturation_risk = if(p95 > 85, "HIGH", else: if(p95 > 70, "MEDIUM", else: "LOW"))
| filter isNotNull(p95)
| sort p95 desc
| fields dt.smartscape.host, p95, saturation_risk

Days to Saturation Forecast

timeseries cpu = avg(dt.host.cpu.usage), from: now()-30d, interval: 1d, by: {dt.smartscape.host}
| fieldsAdd current = arrayLast(cpu)
| fieldsAdd daily_growth = (arrayLast(cpu) - arrayFirst(cpu)) / 30
| filter isNotNull(current)
| fieldsAdd days_to_saturation = if(daily_growth > 0, toLong((90 - current) / daily_growth), else: 9999)
| sort days_to_saturation asc
| limit 20
| fields dt.smartscape.host, current, daily_growth, days_to_saturation

Anomaly Scoring

timeseries cpu = avg(dt.host.cpu.usage), from: now()-24h, interval: 1h, by: {dt.smartscape.host}
| fieldsAdd baseline_avg = arrayAvg(cpu)
| fieldsAdd current = arrayLast(cpu)
| fieldsAdd anomaly_score = if(isNotNull(current) and isNotNull(baseline_avg), abs(current - baseline_avg), else: 0)
| sort anomaly_score desc
| limit 20
| fields dt.smartscape.host, current, baseline_avg, anomaly_score

Metric Discovery

Before forecasting, discover available metrics by keyword:

metrics from: now() - 1h
| filter contains(metric.key, "cpu")
| summarize count(), by: {metric.key}
| sort `count()` desc

Reference Guides

• references/forecasting-analyzer.md — timeseries-forecast tool: data requirements, parameter reference, interval selection, horizon limits, common pitfalls
• references/capacity-forecasting.md — CPU/memory/disk/K8s saturation forecasts; multi-resource risk scoring; days-to-saturation DQL patterns
• references/anomaly-scoring.md — adaptive-anomaly-detector, seasonal-baseline-anomaly-detector, static-threshold-analyzer; DQL deviation scoring
• references/novelty-detection.md — timeseries-novelty-detection tool: spike, drop, step change, trend onset, and variability change detection; all novelty types; parameter reference; worked examples
• references/trend-detection.md — timeseries-novelty-detection for trend onset and change points; week-over-week joins; growth rate and acceleration detection

Related Skills

• dt-dql-essentials — DQL syntax, timeseries command rules, array function reference
• dt-obs-hosts — Host and process metrics catalog
• dt-obs-services — Service RED metrics for service-level trend analysis
• dt-obs-problems — Davis AI problem history for anomaly correlation