Models

From simple forecasts to forecasting intelligence

A progressive hierarchy: each model adds methodological sophistication. Higher levels mean more complexity — not guaranteed performance.

Model progression

From simple forecasts to forecasting intelligence.

Glossover each model to see what it models, why it is included, and how its sophistication changes. Higher levels mean more methodological sophistication — not guaranteed performance.

00 Naïve Baseline Repeats the most recent observed demand as the forecast.

Why included: Establishes the minimum benchmark that more sophisticated models must outperform.

Sophistication: Zero model parameters; minimal “intelligence”.

Role in experiment: The floor of the comparison.

01 Simple Exponential Smoothing Statistical Models a level with exponentially weighted past observations.

Why included: Baseline statistical method for level-based demand without strong trend or seasonality.

Sophistication: One smoothing parameter; univariate.

Role in experiment: First statistical rung.

02 Holt Statistical Adds a trend component to exponential smoothing.

Why included: Tests whether capturing a trend improves retail forecasts.

Sophistication: Two parameters; univariate.

Role in experiment: Introduces trend.

03 Holt-Winters Statistical Adds a seasonal component (level + trend + seasonality).

Why included: Retail demand is often weekly-seasonal; this captures that structure.

Sophistication: Three smoothing parameters + season length.

Role in experiment: Introduces seasonality.

04 ARIMA Statistical Models temporal dependence via autoregressive, differencing and moving-average terms.

Why included: A classical, statistically principled approach with interpretable diagnostics.

Sophistication: Fitted p, d, q order per series; more formal model selection.

Role in experiment: Classical dependence modelling.

05 SARIMA Statistical Extends ARIMA with seasonal components (P, D, Q).

Why included: Combines ARIMA’s power with explicit seasonal structure.

Sophistication: Largest parameter set among statistical models; slowest to fit.

Role in experiment: Most sophisticated statistical forecaster.

06 XGBoost Machine Learning Gradient-boosted trees over lag, rolling-statistic, calendar, price and promotion features.

Why included: Represents nonlinear relationships and uses exogenous drivers unavailable to univariate models.

Sophistication: Feature count + tree ensemble; more data and compute required.

Role in experiment: Primary machine-learning comparison.

07 Small Local LLM Generative AI A small language model run locally (Ollama), evaluated in two distinct roles.

Why included: Explores whether generative AI adds forecasting or decision-support value — without assuming it wins.

Sophistication: Highest complexity, opaque, external model + compute needed.

Role in experiment: Experimental; evaluated, not presumed superior.

The progression represents increasing forecasting sophistication. Whether that sophistication produces better accuracy or better inventory outcomes is exactly what the experiment tests.

Complexity, not a score

Quantified, honestly.

The hierarchy is an ordinal intelligence level (0–7) used as a position on the sophistication axis. Measurable characteristics are recorded per model — parameter/feature count, training and inference time, compute, and interpretability — so the accuracy-versus-effort trade-off is visible. No single invented "AI score" is produced.

The central experiment then asks whether climbing this ladder actually buys better accuracy and better inventory decisions.