Trent Turner
Demand Analytics / Study 04 of 07

Local Demand Momentum

Testing whether recent demand improves forecasts beyond annual seasonality

Question
Can recent normalized demand capture the rate of change as well as the current level?
Method
Six completed weeks of seasonally normalized demand.
Evidence
2026 sensitivity check: rising-season MAE 1.27 vs. 1.47; declining-season 1.92 vs. 1.80, both vs. recent 4-week benchmark.
Decision
Retain momentum; prioritize post-peak adaptation for further testing.
01

Business Question

Can recent normalized demand capture the rate of change as well as the current level?

02

Why It Matters

A flat rolling mean is slow when demand is rising or falling quickly. Seasonal normalization separates the recurring annual slope from current-year momentum.

03

Data / Method

Estimate a local linear endpoint across the previous six normalized observations. The documented weights are −4, −1, 2, 5, 8, and 11, divided by 21.

04

Analysis

Six completed weeks, one local endpoint

Normalized ratio · 2026 W15–W20 approximation

Normalized observationsSix-week local linear fit

Illustrates the documented local-trend operation using workbook observations. The endpoint at W20 supplies the relative-level estimate for the next forecast; this is not a momentum-only validation.

View chart data
WeekNormalized observationsSix-week local linear fit
150.570.58
160.730.57
170.530.56
180.400.54
190.300.53
200.770.52
Method detail
Lₜ = (−4rₜ₋₆ − rₜ₋₅ + 2rₜ₋₄ + 5rₜ₋₃ + 8rₜ₋₂ + 11rₜ₋₁) / 21

This is a local trend estimate, not a positive-weight moving average. Recent observations receive progressively greater weight. The final forecast floors the relative estimate at 0.01.

05

Results

Rising season · W01–W20

2026 calendar-alignment sensitivity check · approximate MAE · weekly New Customers

Adaptive
1.27
Recent four-week
1.47

Post-peak adaptation: the primary failure mode

2026 calendar-alignment sensitivity check · approximate MAE · weekly New Customers

Adaptive
1.92
Recent four-week
1.80
06

Interpretation

Post-peak adaptation emerged as the primary failure mode in this sensitivity check. In W21–W36, adaptive MAE was 1.92 versus 1.80 for the recent four-week benchmark: the simpler recent-history estimate adjusted faster during decline. Next, test post-peak adaptation using exact daily alignment and chronological validation.

07

Limitations

The phase results evaluate the full architecture under an overlap-weighted calendar approximation. They are directional evidence, not a standalone momentum ablation or exact 2026 validation.

08

How this fits into the forecasting system

Retained component: local momentum helps the adaptive level respond to changing demand. Post-peak behavior remains a refinement question.

Next: Economic Demand Signals

Full case study

Download Full Case Study ↓