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Trend Analysis

skill-ericwang915-data-scientist-skills-trend-analysis · by ericwang915

Detect and analyze time-based trends: seasonality, changepoints, decomposition, and growth rates. Use when analyzing metrics over time, detecting structural changes, or understanding periodic patterns.

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Install

$ agentstack add skill-ericwang915-data-scientist-skills-trend-analysis

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

Trend Analysis

Purpose

Identify, decompose, and interpret temporal patterns in your data — trends, seasonality, cyclicality, and structural changes.

How It Works

Step 1: Time Series Profiling

  • Frequency detection (daily, weekly, monthly, quarterly)
  • Missing timestamps and gap analysis
  • Stationarity testing (ADF test, KPSS test)

Step 2: Decomposition

  • Additive decomposition: Trend + Seasonal + Residual (stable seasonality)
  • Multiplicative decomposition: Trend × Seasonal × Residual (growing seasonality)
  • STL decomposition: Robust to outliers, flexible seasonal adjustment

Step 3: Pattern Detection

  • Seasonality: Day-of-week, monthly, quarterly, annual patterns
  • Changepoints: Structural breaks in trend or variance (PELT, Bayesian)
  • Anomalies: Time points that deviate significantly from the trend/season
  • Growth rate: Period-over-period, YoY, CAGR

Step 4: Interpretation

  • What's driving the trend? (correlation with external events)
  • Are seasonal patterns stable or evolving?
  • What caused changepoints? (product launches, market events)

Usage Examples

"Analyze the trend in our daily active users over the past 12 months —
is there seasonality? Any structural changes?"
"Decompose our monthly revenue into trend and seasonal components"

Output Format

  • Trend Summary: Direction, growth rate, confidence interval
  • Decomposition Charts: Trend, seasonal, residual components
  • Changepoint Report: Detected breaks with dates and magnitude
  • Seasonality Profile: Pattern strength by period
  • Python Code: Reproducible analysis script

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.

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