- name
- forecasting-techniques
- description
- Project future using time series, derived demand, and expert opinion methods. Use for market sizing, growth projections, and revenue planning.
Forecasting Techniques
Metadata
- Name: forecasting-techniques
- Description: Multiple methods for projecting future values
- Triggers: forecasting, projections, growth rate, CAGR, market prediction
Instructions
Apply forecasting techniques to project $ARGUMENTS into the future.
Choose appropriate method based on data availability and context.
Framework
Three Main Approaches
| Method | Data Required | Time Horizon | Precision | Best For |
|---|---|---|---|---|
| Time Series Extrapolation | 5-10 years of historical | Short-medium | High | Stable environments |
| Derived Demand | Proxy variables, cross-correlation | Short-medium | Medium | Related markets |
| Expert Opinion | Structured surveys | Any | Low | New products |
1. Time Series Extrapolation
Trend Analysis
- Simple growth rate: Compound annual growth (CAGR)
- Linear regression: Straight line fit to historical data
- Moving average: Smooths volatility, lags trends
- Exponential smoothing: Recent trends weighted more heavily
Steps:
- Gather historical data (3+ years preferred)
- Analyze patterns (cycles, seasonality, trends)
- Choose model (CAGR, regression, etc.)
- Apply to future periods
- Validate against expert opinion
Example Output:
Year | Historical | Projected | Growth Rate |
|------|------------|------------|-------------|
| 2023 | $100 M | - | - |
| 2024 | $115 M | +15% | CAGR = 15% |
| 2025 | $132 M | +15% | CAGR = 15% |
| 2026 | $152 M | +15% | CAGR = 15% |
| 2027 | $175 M | +15% | CAGR = 15% |2. Derived Demand
Proxy Methodology
- Identify proxy variable that correlates with demand
- Use readily available data with reliable trend
- Apply correlation coefficient
- Adjust for unique factors
Examples:
- GDP growth as proxy for consumer spending
- Housing starts as proxy for home goods
- Demographics for category-specific demand
Steps:
- Identify correlation (r² should be > 0.5)
- Gather proxy data
- Apply coefficient
- Adjust for local factors
- Add confidence intervals
3. Expert Opinion
Structured Survey Method
- Multiple expert interviews
- Weighted by expertise or track record
- Delphi technique (iterative rounds)
- Scenario-based questioning
Advantages:
- Captures qualitative insights
- Accounts for disruptive changes
- Incorporates expert judgment
Process:
- Define forecasting questions
- Select experts (diverse backgrounds)
- Conduct interviews (structured format)
- Aggregate with weighting
- Present scenarios (base, optimistic, pessimistic)
- Review and iterate if needed
Output Process
- Define scope - What's being forecasted?
- Select method - Based on data and time horizon
- Gather inputs - Historical data, drivers, expert inputs
- Apply technique - Run the chosen method
- Calculate projections - For each year/period
- Validate - Cross-check with other methods
- Add scenarios - Best, base, worst case
- Document assumptions - Clearly state all key inputs
Output Format
## Forecasting Analysis: [Subject]
### Forecast Methodology
**Method Used:** [Time Series/Derived Demand/Expert Opinion]
**Time Horizon:** [Years]
**Base Year:** [Year]
**Data Quality:** [High/Medium/Low]
---
### Projections
| Metric | 2024 | 2025 | 2026 | 2027 | 2028 | CAGR |
|--------|--------|--------|--------|--------|--------|------|
| Revenue | $X M | $Y M | $Z M | $W M | $V M | % |
| Growth | X% | Y% | Z% | W% | % |
---
### Key Drivers
| Driver | Impact | Uncertainty | Scenario Impact |
|--------|---------|-----------------|--------------|
| [Driver 1] | High | Medium | [Description] |
| [Driver 2] | Medium | Low | [Description] |
| [Driver 3] | Low | High | [Description] |
---
### Scenarios
| Scenario | 2028 Revenue | Probability | Key Assumptions |
|----------|----------------|------------------|----------------|
| **Base** | $X M | 50% | [Assumptions] |
| **Optimistic** | $Y M | 30% | [Assumptions] |
| **Pessimistic** | $W M | 70% | [Assumptions] |
---
### Confidence Intervals
| Metric | Low | Base | High | Confidence |
|--------|------|------|------|------|----------|
| 2028 Revenue | $X ± Y% | $Z M | $W M | 80% |Tips
- Triangulate methods when possible
- Use multiple methods for cross-validation
- Be explicit about assumptions - don't hide them
- Present confidence intervals for transparency
- Consider mean reversion - growth rates tend toward averages
- Validate with real outcomes when available
- Document track record of forecasts - improve over time
References
- Makridakis, Spyros. *Business Forecasting*. 1998.
- Armstrong, J. Scott. *Principles of Forecasting*. 2001.
- Wikipedia. "Forecasting - Methods and Applications" (multiple sources)