---
name: Trend Analysis
description: This skill should be used when the user asks to "identify trends", "analyze market trends", "trend forecasting", "macro trends", "micro trends", "emerging patterns", "future projections", "industry trends", or needs guidance on trend identification, pattern recognition, or market forecasting methodologies.
version: 0.1.0
---

# Trend Analysis

## Overview

Trend analysis identifies patterns of change over time to anticipate future market conditions. This skill covers methodologies for discovering, validating, and projecting trends at macro and micro levels.

## Trend Categories

### Macro Trends (3-10+ years)
Large-scale shifts affecting multiple industries:
- **Economic**: Interest rates, inflation, employment
- **Technological**: AI, blockchain, quantum computing
- **Social**: Demographics, values, behaviors
- **Environmental**: Climate, sustainability, resources
- **Political**: Regulation, trade, governance

### Micro Trends (1-3 years)
Industry or segment-specific patterns:
- Feature adoption curves
- Pricing model shifts
- Channel preferences
- Buying behavior changes
- Competitive dynamics

### Emerging Signals (< 1 year)
Early indicators of potential trends:
- Startup activity
- Patent filings
- Research papers
- Early adopter behavior
- Influencer attention

## Three-Valued Trend Logic

From the trend-based modeling research, apply minimal-information quantifiers:

**INC (Increasing)**
- Measurable upward movement
- Multiple confirming signals
- Example: "AI adoption growing 40% YoY"

**DEC (Decreasing)**
- Measurable downward movement
- Multiple confirming signals
- Example: "On-premise deployments declining 15% annually"

**CONST (Constant)**
- No significant directional movement
- OR insufficient data to determine direction
- Example: "Market share stable at ~30%"

### Correlation-to-Trend Conversion

Convert data relationships to trend indicators:
- Positive correlation (r > 0.3) → INC relationship
- Negative correlation (r < -0.3) → DEC relationship
- Weak correlation (-0.3 < r < 0.3) → CONST relationship

## Trend Identification Process

### Step 1: Signal Gathering
Collect data points from:
- Industry reports and analyses
- News and publications
- Patent databases
- Job posting trends
- Search interest (Google Trends)
- Social media discussions
- Conference topics
- Funding announcements

### Step 2: Pattern Recognition
Look for:
- Consistent direction over 3+ time periods
- Acceleration/deceleration in rate of change
- Cross-industry convergence
- Discontinuities and inflection points

### Step 3: Validation
Confirm trends through:
- Multiple independent sources
- Expert opinions
- Historical analogies
- Quantitative data where available

### Step 4: Classification
Assign trend direction:
- Determine INC/DEC/CONST
- Note confidence level
- Document supporting evidence

### Step 5: Projection
Extend trends forward considering:
- Historical trajectory
- Accelerating/decelerating forces
- Potential disruptions
- Saturation points

## Transitional Scenario Graphs

Create Mermaid state diagrams showing possible futures:

```mermaid
stateDiagram-v2
    [*] --> CurrentState

    CurrentState --> GrowthPath: INC indicators strong
    CurrentState --> StablePath: CONST indicators
    CurrentState --> DeclinePath: DEC indicators

    GrowthPath --> AcceleratingGrowth: Network effects kick in
    GrowthPath --> DeceleratingGrowth: Market saturation

    StablePath --> NicheEquilibrium: Specialized use cases
    StablePath --> DisruptionVulnerable: Tech shift pending

    DeclinePath --> ManagedDecline: Harvest strategy
    DeclinePath --> RapidObsolescence: Substitute adoption
```

### Terminal Scenarios

Identify equilibrium states where trends stabilize:
- What market structure emerges?
- Which players win/lose?
- What trade-offs must organizations accept?

## Trend Quality Assessment

Rate trend confidence:

| Confidence | Evidence Required |
|------------|-------------------|
| High | 3+ independent sources, quantitative data, expert consensus |
| Medium | 2+ sources, qualitative signals, some disagreement |
| Low | Single source, early signals, speculative |

## Output Structure

```markdown
## Trend Analysis Summary

### Macro Trends
| Trend | Direction | Confidence | Timeframe |
|-------|-----------|------------|-----------|
| [Name] | INC/DEC/CONST | High/Med/Low | X years |

### Micro Trends
| Trend | Direction | Confidence | Timeframe |
|-------|-----------|------------|-----------|
| [Name] | INC/DEC/CONST | High/Med/Low | X months |

### Emerging Signals
- [Signal 1]: [Potential implication]
- [Signal 2]: [Potential implication]

## Transitional Scenario Graph
[Mermaid diagram]

## Terminal Scenarios
1. **[Scenario Name]**: [Description and conditions]
2. **[Scenario Name]**: [Description and conditions]

## Implications
- [Implication 1]
- [Implication 2]

## Monitoring Indicators
- [Metric to track]
- [Metric to track]
```

## Best Practices

- **Multiple timeframes**: Analyze short, medium, and long-term
- **Cross-validate**: Use diverse sources and methods
- **Update regularly**: Trends can shift; review quarterly
- **Note uncertainty**: Distinguish confidence levels clearly
- **Watch for reversals**: Monitor for trend changes
- **Consider second-order effects**: What does the trend cause?

## Common Pitfalls

- Confirmation bias (seeing trends you expect)
- Recency bias (overweighting recent data)
- Survivorship bias (only seeing successful trends)
- Extrapolation without limits (trends don't continue forever)
- Ignoring counter-trends (opposing forces)

## Additional Resources

For detailed methodologies, see:
- `references/trend-signals.md` - Signal identification techniques
- `references/scenario-planning.md` - Scenario development methods
- `examples/trend-report.md` - Sample trend analysis