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QC13_TimeEquivalentValue

Detect unchanged values in time-series data across specified fields and time formats.

541|171|Updated May 3, 2018
Search Tags:#quality control#data quality#manufacturing#time-series#anomaly detection
One-click install
npx skills add https://github.com/cas-bigdatalab/piflow --skill qc13-timeequivalentvalue
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: QC13_TimeEquivalentValue
Source: https://github.com/cas-bigdatalab/piflow/tree/main/workspace/skills/QC13_TimeEquivalentValue
Command: npx skills add https://github.com/cas-bigdatalab/piflow --skill qc13-timeequivalentvalue

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill efficiently detects anomalies in time-series data by identifying when key metrics have been consistently unchanged for extended periods.

Core Features & Use Cases

  • Time-Series Anomaly Detection: Monitors and flags data points where a value has remained the same over a defined threshold.
  • Customizable Checks: Allows users to define specific conditions (field, count) and time formats to analyze.
  • Use Case: Perfect for quality control in manufacturing, environmental monitoring, or any situation requiring the detection of long-term stability in metrics.

Quick Start

Utilize the QC13_TimeEquivalentValue Skill to check the 'sensor_data.csv' file for stability across the 'temperature' field with the criteria 'temperature,3' and the time format 'YYYY-MM-DD'.

Frequently Asked Questions about QC13_TimeEquivalentValue

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect anomalies in time-series data when sensor values remain unchanged?▼

Time-series anomaly detection for unchanged values is handled by checking a specific field against a defined count threshold to flag metrics that remain consistently stable over extended periods.

Can I customize the time format and parameters for manufacturing quality control checks?▼

Manufacturing quality control checks support customizable parameters, allowing you to define specific target fields, stable value counts, and exact time formats like 'YYYY-MM-DD' for analysis.

What is the best way to monitor long-term metric stability in a CSV file?▼

Monitoring long-term metric stability in a CSV file involves analyzing a target field with a specified unchanged count threshold and applying a designated time format to pinpoint anomalies.

How do I check for consistent temperature readings in environmental monitoring data?▼

Checking for consistent temperature readings requires analyzing the time-series data by setting a specific unchanged count threshold for the temperature field alongside a defined time format.

Does this anomaly detection approach work for any data domain beyond manufacturing?▼

Anomaly detection for unchanged values works across any domain requiring the monitoring of long-term metric stability, including environmental monitoring and general quality control.

Why do I need to specify a count threshold for time-series anomaly detection?▼

Specifying a count threshold for time-series anomaly detection is required to establish the exact number of consecutive unchanged data points that define a stable period anomaly.

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