Growth Modeling and Forecasting

BusinessMath Quarterly Series

11 min read

Part 9 of 12-Week BusinessMath Series

What You’ll Learn

Calculating growth rates (simple and CAGR)
Fitting trend models (linear, exponential, logistic)
Extracting and applying seasonal patterns
Building complete forecasting workflows
Choosing the right approach for your data

The Problem

Business planning requires forecasting: Will we hit our revenue target? How many users will we have next quarter? What should our headcount plan look like?

Forecasting means understanding growth patterns:

Growth rates: How fast are we growing?
Trend models: What’s the underlying trajectory?
Seasonality: Do we have recurring patterns (Q4 spike, summer slump)?

Building robust forecasts manually requires statistical knowledge, careful data handling, and combining multiple techniques. You need systematic tools for growth analysis and forecasting.

The Solution

BusinessMath provides comprehensive growth modeling including growth rate calculations, trend fitting, and seasonality extraction.

Growth Rates

Calculate simple and compound growth:

import BusinessMath

// Simple growth rate
let growth = try growthRate(from: 100_000, to: 120_000)
// Result: 0.20 (20% growth)

// Negative growth (decline)
let decline = try growthRate(from: 120_000, to: 100_000)
// Result: -0.1667 (-16.67% decline)

Formula:

Growth Rate = (Ending / Beginning) - 1

Compound Annual Growth Rate (CAGR)

CAGR smooths out volatility to show steady equivalent growth:

// Revenue: $100k → $110k → $125k → $150k over 3 years
let compoundGrowth = cagr(
    beginningValue: 100_000,
    endingValue: 150_000,
    years: 3
)
// Result: ~0.1447 (14.47% per year)

// Verify: does 14.47% compound for 3 years give $150k?
let verification = 100_000 * pow((1 + compoundGrowth), 3.0)
// Result: ~150,000 ✓

Formula:

CAGR = (Ending / Beginning)^(1/years) - 1

The insight: Revenue was volatile year-to-year ($10k, then $15k, then $25k growth), but CAGR shows the equivalent steady rate: 14.47% annually.

Applying Growth

Project future values:

// Project $100k base with 15% annual growth for 5 years
let projection = applyGrowth(
    baseValue: 100_000,
    rate: 0.15,
    periods: 5,
    compounding: .annual
)
// Result: [100k, 115k, 132.25k, 152.09k, 174.90k, 201.14k]

Compounding Frequencies

Different frequencies affect growth:

let base = 100_000.0
let rate = 0.12  // 12% annual rate
let years = 5

// Annual: 12% once per year
let annual = applyGrowth(baseValue: base, rate: rate, periods: years, compounding: .annual)
print(annual.last!.number(0))
// Final: ~176,234

// Quarterly: 3% four times per year
let quarterly = applyGrowth(baseValue: base, rate: rate, periods: years * 4, compounding: .quarterly)
print(quarterly.last!.number(0))
// Final: ~180,611 (higher due to more frequent compounding)

// Monthly: 1% twelve times per year
let monthly = applyGrowth(baseValue: base, rate: rate, periods: years * 12, compounding: .monthly)
print(monthly.last!.number(0))
// Final: ~181,670

// Continuous: e^(rt)
let continuous = applyGrowth(baseValue: base, rate: rate, periods: years, compounding: .continuous)
print(continuous.last!.number(0))
// Final: ~182,212 (theoretical maximum)

The insight: More frequent compounding increases final value. Continuous compounding is the mathematical limit.

Trend Models

Trend models fit mathematical functions to historical data for forecasting.

Linear Trend

Models constant absolute growth:

// Historical revenue shows steady ~$5k/month increase
let periods_linearTrend = (1...12).map { Period.month(year: 2024, month: $0) }
let revenue_linearTrend: [Double] = [100, 105, 110, 108, 115, 120, 118, 125, 130, 128, 135, 140]

let historical_linearTrend = TimeSeries(periods: periods_linearTrend, values: revenue_linearTrend)

// Fit linear trend
var trend_linearTrend = LinearTrend
            
              () try trend_linearTrend.fit(to: historical_linearTrend) // Project 6 months forward let forecast_linearTrend = try trend_linearTrend.project(periods: 6) print(forecast_linearTrend.valuesArray.map({$0.rounded()})) // Result: [142, 145, 148, 152, 155, 159] (approximately)

Formula:

y = mx + b

Where:
- m = slope (rate of change)
- b = intercept (starting value)

Best for:

Steady absolute growth (adding same $ each period)
Short-term forecasts
Linear relationships

Exponential Trend

Models constant percentage growth:

// Revenue doubling every few years
let periods_exponentialTrend = (0..<10).map { Period.year(2015 + $0) }
let revenue_exponentialTrend: [Double] = [100, 115, 130, 155, 175, 200, 235, 265, 310, 350]

let historical_exponentialTrend = TimeSeries(periods: periods_exponentialTrend, values: revenue_exponentialTrend)

// Fit exponential trend
var trend_exponentialTrend = ExponentialTrend
            
              () try trend_exponentialTrend.fit(to: historical_exponentialTrend) // Project 5 years forward let forecast_exponentialTrend = try trend_exponentialTrend.project(periods: 5) // Result: [407, 468, 538, 619, 713]

Formula:

y = a × e^(bx)

Where:
- a = initial value
- b = growth rate
- e = Euler's number (2.71828...)

Best for:

Constant percentage growth (e.g., 15% per year)
Long-term trends
Compound growth scenarios

Logistic Trend

Models growth approaching a capacity limit (S-curve):

// User adoption: starts slow, accelerates, then plateaus
let periods_logisticTrend = (0..<24).map { Period.month(year: 2023 + $0/12, month: ($0 % 12) + 1) }
let users_logisticTrend: [Double] = [100, 150, 250, 400, 700, 1200, 2000, 3500, 5500, 8000,
						11000, 14000, 17000, 19500, 21500, 23000, 24000, 24500,
						24800, 24900, 24950, 24970, 24985, 24990]

let historical_logisticTrend = TimeSeries(periods: periods_logisticTrend, values: users_logisticTrend)

// Fit logistic trend with capacity of 25,000 users
var trend_logisticTrend = LogisticTrend
            
              (capacity: 25_000) try trend_logisticTrend.fit(to: historical_logisticTrend) // Project 12 months forward let forecast_logisticTrend = try trend_logisticTrend.project(periods: 12) // Result: Approaches but never exceeds 25,000

Formula:

y = L / (1 + e^(-k(x-x₀)))

Where:
- L = capacity (maximum value)
- k = growth rate
- x₀ = midpoint of curve

Best for:

Market saturation scenarios
Product adoption curves
SaaS user growth with market limits
Biological growth (population with carrying capacity)

Seasonality

Extract and apply recurring patterns.

Seasonal Indices

Calculate seasonal factors:

// Quarterly revenue with Q4 holiday spike
let periods = (0..<12).map { Period.quarter(year: 2022 + $0/4, quarter: ($0 % 4) + 1) }
let revenue: [Double] = [100, 120, 110, 150,  // 2022
                         105, 125, 115, 160,  // 2023
                         110, 130, 120, 170]  // 2024

let ts = TimeSeries(periods: periods, values: revenue)

// Calculate seasonal indices (4 quarters per year)
let indices = try seasonalIndices(timeSeries: ts, periodsPerYear: 4)
print(indices.map({"\($0.number(2))"}).joined(separator: ", "))
// Result: [~0.85, ~1.00, ~0.91, ~1.24]

Interpretation:

Q1: 0.85 → 15% below average (post-holiday slump)
Q2: 1.00 → Average
Q3: 0.91 → 9% below average (summer slowdown)
Q4: 1.24 → 24% above average (holiday spike!)

Complete Forecasting Workflow

Combine all techniques:

// 1. Load historical data
let historical = TimeSeries(periods: historicalPeriods, values: historicalRevenue)

// 2. Extract seasonal pattern
let seasonalIndices = try seasonalIndices(timeSeries: historical, periodsPerYear: 4)

// 3. Deseasonalize to reveal underlying trend
let deseasonalized = try seasonallyAdjust(timeSeries: historical, indices: seasonalIndices)

// 4. Fit trend model to deseasonalized data
var trend = LinearTrend
            
              () try trend.fit(to: deseasonalized) // 5. Project trend forward let forecastPeriods = 4 // Next 4 quarters let trendForecast = try trend.project(periods: forecastPeriods) // 6. Reapply seasonality to trend forecast let seasonalForecast = try applySeasonal(timeSeries: trendForecast, indices: seasonalIndices) // 7. Present forecast for (period, value) in zip(seasonalForecast.periods, seasonalForecast.valuesArray) { print("\(period.label): \(value.currency())") }

This workflow:

Extracts the recurring seasonal pattern
Removes it to see the underlying growth trend
Fits a trend model to clean data
Projects that trend forward
Reapplies the seasonal pattern to the forecast
Produces realistic forecasts that account for both trend and seasonality

Choosing the Right Approach

Decision Tree

Step 1: Does your data have seasonality?

Yes → Extract seasonal pattern first
No → Skip to trend modeling

Step 2: What kind of growth pattern?

Constant $ per period → Linear Trend
Constant % per period → Exponential Trend
Growth approaching limit → Logistic Trend

Step 3: How much history do you have?

< 2 full cycles → Use simple growth rates
2-3 cycles → Linear or exponential trend
3+ cycles → Full decomposition with seasonality

Step 4: What’s your forecast horizon?

Short-term (1-3 periods) → Any model works
Medium-term (4-8 periods) → Trend models with seasonality
Long-term (9+ periods) → Be cautious, validate assumptions

Try It Yourself

Click to expand full playground code

import BusinessMath

// Simple growth rate
let growth = try growthRate(from: 100_000, to: 120_000)
// Result: 0.20 (20% growth)

// Negative growth (decline)
let decline = try growthRate(from: 120_000, to: 100_000)
// Result: -0.1667 (-16.67% decline)


// Revenue: $100k → $110k → $125k → $150k over 3 years
let compoundGrowth = cagr(
	beginningValue: 100_000,
	endingValue: 150_000,
	years: 3
)
// Result: ~0.1447 (14.47% per year)

// Verify: does 14.47% compound for 3 years give $150k?
let verification = 100_000 * pow((1 + compoundGrowth), 3.0)
// Result: ~150,000 ✓

	// Project $100k base with 15% annual growth for 5 years
	let projection = applyGrowth(
		baseValue: 100_000,
		rate: 0.15,
		periods: 5,
		compounding: .annual
	)
	// Result: [100k, 115k, 132.25k, 152.09k, 174.90k, 201.14k]

let base = 100_000.0
let rate = 0.12  // 12% annual rate
let years = 5

// Annual: 12% once per year
let annual = applyGrowth(baseValue: base, rate: rate, periods: years, compounding: .annual)
print(annual.last!.number(0))
// Final: ~176,234

// Quarterly: 3% four times per year
let quarterly = applyGrowth(baseValue: base, rate: rate, periods: years * 4, compounding: .quarterly)
print(quarterly.last!.number(0))
// Final: ~180,611 (higher due to more frequent compounding)

// Monthly: 1% twelve times per year
let monthly = applyGrowth(baseValue: base, rate: rate, periods: years * 12, compounding: .monthly)
print(monthly.last!.number(0))
// Final: ~181,670

// Continuous: e^(rt)
let continuous = applyGrowth(baseValue: base, rate: rate, periods: years, compounding: .continuous)
print(continuous.last!.number(0))
// Final: ~182,212 (theoretical maximum)

	// Historical revenue shows steady ~$5k/month increase
	let periods_linearTrend = (1...12).map { Period.month(year: 2024, month: $0) }
	let revenue_linearTrend: [Double] = [100, 105, 110, 108, 115, 120, 118, 125, 130, 128, 135, 140]

	let historical_linearTrend = TimeSeries(periods: periods_linearTrend, values: revenue_linearTrend)

	// Fit linear trend
	var trend_linearTrend = LinearTrend
              
                () try trend_linearTrend.fit(to: historical_linearTrend) // Project 6 months forward let forecast_linearTrend = try trend_linearTrend.project(periods: 6) print(forecast_linearTrend.valuesArray.map({$0.rounded()})) // Result: [142, 145, 148, 152, 155, 159] (approximately) // Revenue doubling every few years let periods_exponentialTrend = (0..<10).map { Period.year(2015 + $0) } let revenue_exponentialTrend: [Double] = [100, 115, 130, 155, 175, 200, 235, 265, 310, 350] let historical_exponentialTrend = TimeSeries(periods: periods_exponentialTrend, values: revenue_exponentialTrend) // Fit exponential trend var trend_exponentialTrend = ExponentialTrend
                
                  () try trend_exponentialTrend.fit(to: historical_exponentialTrend) // Project 5 years forward let forecast_exponentialTrend = try trend_exponentialTrend.project(periods: 5) print(forecast_exponentialTrend.valuesArray.map({$0.rounded()})) // Result: [407, 468, 538, 619, 713] // User adoption: starts slow, accelerates, then plateaus let periods_logisticTrend = (0..<24).map { Period.month(year: 2023 + $0/12, month: ($0 % 12) + 1) } let users_logisticTrend: [Double] = [100, 150, 250, 400, 700, 1200, 2000, 3500, 5500, 8000, 11000, 14000, 17000, 19500, 21500, 23000, 24000, 24500, 24800, 24900, 24950, 24970, 24985, 24990] let historical_logisticTrend = TimeSeries(periods: periods_logisticTrend, values: users_logisticTrend) // Fit logistic trend with capacity of 25,000 users var trend_logisticTrend = LogisticTrend
                  
                    (capacity: 25_000) try trend_logisticTrend.fit(to: historical_logisticTrend) // Project 12 months forward let forecast_logisticTrend = try trend_logisticTrend.project(periods: 12) print(forecast_logisticTrend.valuesArray.map({$0.rounded()})) // Result: Approaches but never exceeds 25,000 // Quarterly revenue with Q4 holiday spike let periods = (0..<12).map { Period.quarter(year: 2022 + $0/4, quarter: ($0 % 4) + 1) } let revenue: [Double] = [100, 120, 110, 150, // 2022 105, 125, 115, 160, // 2023 110, 130, 120, 170] // 2024 let ts = TimeSeries(periods: periods, values: revenue) // Calculate seasonal indices (4 quarters per year) let indices = try seasonalIndices(timeSeries: ts, periodsPerYear: 4) print(indices.map({"\($0.number(2))"}).joined(separator: ", ")) // Result: [~0.85, ~1.00, ~0.91, ~1.24] // 1. Load historical data let historical = TimeSeries(periods: historicalPeriods, values: historicalRevenue) // 2. Extract seasonal pattern let seasonalIndices = try seasonalIndices(timeSeries: historical, periodsPerYear: 4) // 3. Deseasonalize to reveal underlying trend let deseasonalized = try seasonallyAdjust(timeSeries: historical, indices: seasonalIndices) // 4. Fit trend model to deseasonalized data var trend = LinearTrend
                    
                      () try trend.fit(to: deseasonalized) // 5. Project trend forward let forecastPeriods = 4 // Next 4 quarters let trendForecast = try trend.project(periods: forecastPeriods) // 6. Reapply seasonality to trend forecast let seasonalForecast = try applySeasonal(timeSeries: trendForecast, indices: seasonalIndices) // 7. Present forecast for (period, value) in zip(seasonalForecast.periods, seasonalForecast.valuesArray) { print("\(period.label): \(value.currency())") }

→ Full API Reference: BusinessMath Docs – 3.1 Growth Modeling

Modifications to try:

Calculate CAGR for your company’s historical revenue
Fit different trend models and compare predictions
Extract seasonal patterns from your business data

Real-World Application

A SaaS company tracking user growth notices:

Monthly data: 10-15% growth, but volatile
CAGR over 2 years: 12.3% (the smoothed view)
Seasonal pattern: Lower signups in July-August (summer)
Trend model: Logistic with 100k user capacity (market saturation)

Combining these insights produces a forecast that accounts for:

Long-term growth trajectory (logistic curve)
Seasonal dips in summer
Market saturation approaching

This is infinitely more useful than a simple “we’re growing 15%/month” projection.

★ Insight ─────────────────────────────────────

Why Deseasonalize Before Trend Fitting?

If you fit a trend to raw seasonal data, the model gets confused:

Q4 spikes look like acceleration
Q1 dips look like deceleration
The fitted trend becomes wavy instead of smooth

Deseasonalizing first lets you fit a clean trend, then reapply the seasonal pattern to forecasts.

Think of it like removing noise before measuring signal.

─────────────────────────────────────────────────

📝 Development Note

The hardest decision in growth modeling was: Should we make seasonality automatic, or explicit?

Some libraries auto-detect seasonal patterns. Sounds convenient! But it often gets it wrong—detecting false patterns in noise, or missing real patterns in small datasets.

We chose explicit seasonality:

You specify periodsPerYear (4 for quarters, 12 for months)
You inspect the indices before using them
You decide if the pattern makes business sense

This requires one extra line of code, but prevents silent errors. When seasonality extraction fails, you know immediately and can investigate.

The lesson: Convenience features that fail silently are worse than explicit APIs that require judgment.

Related Methodology: The Master Plan (Tuesday) - Planning for API decisions

Next Steps

Coming up next: The Master Plan (Tuesday) - How to organize large projects with AI collaboration.

This week: Revenue modeling (Thursday) and Capital Equipment case study (Friday).

Series Progress:

Week: 3/12
Posts Published: 9/~48
Topics Covered: Foundation + Analysis + Operational Models (starting)
Playgrounds: 8 available

Tagged with: forecasting