Ensemble forecasting is the practice of combining multiple forecasting methods instead of relying on a single model. In demand planning, this matters because no single forecasting method is best for every SKU, customer, location, or lifecycle stage.
Horizon uses ensemble forecasting to evaluate multiple model families and automatically select the best approach at the planning level. The system considers historical accuracy, bias, volatility, intermittency, seasonality, causal signals, and new-product behavior.
Planners do not need to manually pick ARIMA, Holt-Winters, Croston, or machine learning. Horizon evaluates the candidates, applies holdout testing, checks stability, and chooses the forecast that is most suitable for the demand pattern.
The planner still sees the reasoning. Horizon can explain whether a model was selected because of seasonality, intermittent demand, promotional behavior, lifecycle stage, or better holdout performance.
Most businesses do not have one type of demand pattern. A portfolio usually contains stable products, seasonal products, intermittent products, fast-growing products, promotional products, and new products with little history.
A single forecasting method will usually work well for one group and poorly for another. For example, a simple moving average may work for stable items but fail on seasonal products. Croston-style models may help intermittent demand but are not appropriate for fast-moving products.
This is why ensemble forecasting is especially useful for mid-market and enterprise planning teams. It allows the system to adapt by pattern rather than forcing planners to manually decide which method belongs to which item.
Modern ensemble approaches typically include 8-20 methods across categories. Statistical methods include: simple moving average (baseline for stable demand), exponential smoothing variants (single, double, triple/Holt-Winters for different patterns), ARIMA and SARIMA (autoregressive integrated moving average, with seasonal variants), Croston's method (intermittent demand), Syntetos-Boylan-Approximation (improved intermittent demand handling). Machine learning methods include: gradient boosting (XGBoost, LightGBM variants), random forests, neural network approaches (LSTM for time series), Prophet (Facebook's open-source time series approach). Each method has demand patterns it handles well and patterns it handles poorly.
For each SKU, ensemble platforms typically: train each method on historical data, evaluate each method on holdout data (recent history reserved from training), select the method with best holdout accuracy, sometimes use multiple methods in weighted combination if no single method clearly dominates. The selection happens automatically — planners don't choose methods per SKU manually.
The selection logic typically uses metrics like MAPE, WMAPE, or MAD on holdout data. Some implementations also consider stability (method consistency across multiple holdout periods) to avoid selecting methods that look great on one period but vary widely.
Demand patterns change. A SKU best forecast by exponential smoothing today may shift to seasonal patterns over 6 months, making Holt-Winters more appropriate. Ensemble platforms re-evaluate method selection periodically (typically monthly or quarterly), automatically switching methods when better fits emerge. This continuous re-evaluation is one of the major advantages over single-method platforms — adaptation happens automatically rather than requiring manual reconfiguration.
Ensemble methods can't forecast products without history through standard training. New product introduction (NPI) forecasting requires different approaches: analog-based methods (find similar products, apply their patterns), judgmental forecasting frameworks (structured planner input based on market knowledge), expected lifecycle curves (assumed launch pattern with adjustments). Mature ensemble platforms typically include these NPI-specific approaches alongside the standard methods.
Pure time-series methods forecast based on historical patterns alone. Ensemble approaches often include causal methods that incorporate external factors: pricing, promotional activity, marketing campaigns, weather, economic indicators, market events. These methods can deliver substantial accuracy gains for SKUs with significant causal influences. They require more data infrastructure (the causal factors must be captured and clean) but deliver value when conditions are right.
The strongest ensemble case: portfolios with diverse demand patterns. CPG manufacturers with mix of fast-moving baseline products, seasonal SKUs, promotional items, and new product launches. Industrial manufacturers with mix of standard MTS products, customer-specific configurations, and aftermarket parts. Each pattern benefits from different methods; ensemble captures the variety.
If your current forecasting uses a single method but your portfolio has patterns that method doesn't handle well, ensemble delivers substantial improvement. Example: pure exponential smoothing across a portfolio with significant seasonal SKUs — those seasonal SKUs forecast poorly until methods like Holt-Winters get applied.
Demand patterns evolve. Products launch, mature, decline. Markets shift. Continuous re-evaluation of method fit captures these changes. Single-method approaches require manual reconfiguration when patterns shift, which usually doesn't happen in practice.
Not every situation requires ensemble. Cases where single methods deliver adequate accuracy: homogeneous portfolios (e.g., pure capital equipment with consistent demand patterns), small SKU counts where per-SKU manual method selection is practical, organizations with deep forecasting analytical capability that can manage method selection thoughtfully. The honest qualifier: most mid-market portfolios are diverse enough to benefit from ensemble, but small or homogeneous operations may not.
Not all ensemble forecasting is created equal. Quality differentiators: number of methods included (8-20 typical for mature implementations, fewer suggests limited capability), automation depth (fully automatic per-SKU selection versus partial automation requiring planner choices), transparency (planners can see which method was selected and why, for trust and refinement), stability handling (avoiding method-flipping where selection changes month-to-month based on noise), causal factor integration (ability to incorporate external factors when relevant), NPI handling (specific methods for new product forecasting), continuous re-evaluation cadence (monthly typical for active markets, quarterly for stable markets).
