Predictive analytics for Dynatrace — time series forecasting with the timeseries-forecast tool, capacity saturation planning, trend and anomaly detection across hosts, services, and infrastructure.
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Predictive analytics for Dynatrace — time series forecasting with the timeseries-forecast tool, capacity saturation planning, trend and anomaly detection across hosts, services, and infrastructure.
license
Apache-2.0
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
"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
