Dt Obs Predictive Analytics Agent Skill

Predictive Analytics Skill

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

Analysis Disciplines

| # | Discipline | Use when … |

|---|------------|-----------|

| 1 | Forecast and Prediction | Predicting future metric values for capacity planning, cost estimation, or proactive alerting |

| 2 | Detecting Changes | A metric *shifted* — find when the character of the signal changed, regardless of whether it crossed a limit |

| 3 | Detecting Violations | A metric is *currently out of bounds* — find entities that exceed or fall below an acceptable range |

| 4 | Timeseries Characteristics | Characterizing a signal's seasonality, noise level, and trend before further analysis |

Choosing the Right Detection Tool

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

| Question | Tool | Why |

|----------|------|-----|

| "Did this metric change in the last N hours?" | timeseries-novelty-detection | Detects *when* the signal's character changed (spike, step, trend onset, variability shift) without requiring a known acceptable limit |

| "Which services spiked or dropped recently?" | timeseries-novelty-detection with SPIKE / CHANGE_IN_VALUES | Finds the specific entities and timestamps where change occurred; returns empty for stable signals |

| "When did CPU start trending up?" | timeseries-novelty-detection with TREND_IN_VALUES | Pinpoints the onset of a directional shift |

| "Which hosts are currently above 90% CPU?" | static-threshold-analyzer | Known fixed limit — fire alerts when exceeded | Can also be done with standard DQL queries, but the tool provides built-in violation counting, sliding window and alerting logic |

| "Which services are currently above their usual load?" | adaptive-anomaly-detector | Learns the normal distribution from the data and flags sustained threshold violations |

| "Which services are high right now vs. their weekly pattern?" | seasonal-baseline-anomaly-detector | Accounts for time-of-day/day-of-week patterns before deciding what is anomalous |

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:

| Column | Content |

|--------|---------|

| Rank | 🥇 🥈 🥉 ordered by urgency or magnitude |

| Signal / Entity | Metric name and entity or dimension |

| Last Actual | Most recent non-null value from the historical series |

| Forecast | Point forecast at the end of the horizon |

| Range | Lower – Upper confidence band at the same horizon point |

| Trend | % change from Last Actual to Forecast: 🔴 >+20% / 🟠 +5–20% / 🟢 ±5% stable / 🔵 −5–20% declining / ⚫ <−20% sharp drop |

| Action | ✅ No action / ⚠️ Monitor / 🔴 Act now |

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

• timeseries returns arrays — one value per time slot per entity
• arrayLast(arr) = most recent value; arrayFirst(arr) = oldest
• Growth = (arrayLast - arrayFirst) / number_of_intervals
• Always filter isNotNull(field) before sorting to avoid null ordering issues
• Use toLong() when dividing Long fields to avoid type errors
• 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