# Trend Analysis

> 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.

- **Type:** Skill
- **Install:** `agentstack add skill-ericwang915-data-scientist-skills-trend-analysis`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [ericwang915](https://agentstack.voostack.com/s/ericwang915)
- **Installs:** 0
- **Category:** [Data & Analytics](https://agentstack.voostack.com/c/data-and-analytics)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [ericwang915](https://github.com/ericwang915)
- **Source:** https://github.com/ericwang915/data-scientist-skills/tree/main/ds-exploratory-analysis/skills/trend-analysis

## Install

```sh
agentstack add skill-ericwang915-data-scientist-skills-trend-analysis
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## 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.

- **Author:** [ericwang915](https://github.com/ericwang915)
- **Source:** [ericwang915/data-scientist-skills](https://github.com/ericwang915/data-scientist-skills)
- **License:** MIT

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

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-ericwang915-data-scientist-skills-trend-analysis
- Seller: https://agentstack.voostack.com/s/ericwang915
- Browse the marketplace: https://agentstack.voostack.com/browse

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