Who should use the Detect anomalies workflow?
Teams or solo builders working on business tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Business
A focused workflow that prepares time-series data, applies anomaly detection algorithms, and produces a comprehensive report of detected anomalies for business stakeholders.
Deliverable outcome
An actionable report containing all detected anomalies, severity levels, and recommended next steps has been generated and is ready for delivery to management.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
An actionable report containing all detected anomalies, severity levels, and recommended next steps has been generated and is ready for delivery to management.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Orange Data Mining to historical time-series data has been collected, cleaned, and normalized, establishing a baseline for normal behavior that enables accurate anomaly detection. Then, you pass the output to PagerDuty AIOps to a set of detected anomalies with severity scores and timestamps is generated for further analysis and reporting. Finally, Rose AI is used to an actionable report containing all detected anomalies, severity levels, and recommended next steps has been generated and is ready for delivery to management.
Data Preparation: Analyze time-series data
Historical time-series data has been collected, cleaned, and normalized, establishing a baseline for normal behavior that enables accurate anomaly detection.
Detect anomalies
A set of detected anomalies with severity scores and timestamps is generated for further analysis and reporting.
Reporting: Generate custom reports
An actionable report containing all detected anomalies, severity levels, and recommended next steps has been generated and is ready for delivery to management.
Collect and analyze historical time-series data to identify patterns and baseline statistics that will be used to detect anomalies in subsequent steps.
Proper data preparation is critical for accurate anomaly detection; it establishes the normal behavior against which anomalies are flagged.
Historical time-series data has been collected, cleaned, and normalized, establishing a baseline for normal behavior that enables accurate anomaly detection.
Apply anomaly detection algorithms on the prepared time-series data to identify outliers, irregularities, and unusual patterns that deviate from established norms.
This is the primary step where anomalies are actually identified; the quality of detection determines the value of the entire workflow.
A set of detected anomalies with severity scores and timestamps is generated for further analysis and reporting.
Create a detailed report summarizing the detected anomalies, including visualizations, root cause insights, and recommended actions for stakeholders.
A clear report is essential for stakeholders to understand the anomalies, their potential impact, and the necessary follow-up actions to mitigate risks.
An actionable report containing all detected anomalies, severity levels, and recommended next steps has been generated and is ready for delivery to management.
Timeline Map
§ Before you start
Teams or solo builders working on business tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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