Three cases where compositional modeling beats bottom-up aggregation
January 2026. For peer-reviewed work, see Research.
If you forecast a total and its parts separately, the parts won't add up to the total. This is well known. Most production systems ignore it anyway. They forecast each component independently, reconcile after the fact, and move on.
That is often fine. Sometimes it is not. Here are three situations where I have found it worth building coherence into the model directly rather than patching it downstream.
1. When the total is more predictable than the parts
Revenue mix by currency is a clean example. Total revenue in USD has strong seasonal patterns and relatively stable trends. The share attributable to EUR, GBP, or AUD individually is noisier. Exchange rate movements and regional booking behavior are hard to forecast well. If you forecast each currency's revenue independently, you get reasonable point estimates that sum to something different from your total revenue forecast. Reconciliation fixes the math but introduces artifacts. The adjusted shares inherit noise from both the component and total forecasts.
A compositional model (Dirichlet, logistic-normal, or something in that family) forecasts the shares directly on the simplex, then multiplies by a separate total forecast. The shares sum to one by construction. No reconciliation needed. The gain is that downstream consumers (treasury, FP&A) get numbers that are internally consistent without a post-processing step that nobody fully understands.
2. When substitution effects dominate
Consider a platform where customers choose among product categories and the total number of transactions is roughly fixed in the short run. A spike in Category A usually means a dip in Category B. Independent models do not capture this. They can both go up simultaneously, producing a total that overshoots reality.
Compositional models handle substitution naturally because the shares are jointly modeled. If one share increases, the others must decrease. This is not a side effect. It is the point. The constraint encodes the economic reality that these categories compete for the same pool of transactions.
Bottom-up aggregation can approximate this with a reconciliation step, but the reconciliation is doing the work that the compositional model does by construction. And reconciliation methods (MinT, OLS, etc.) optimize a statistical criterion, not the structural constraint. They get you close, but "close" means your shares sum to 1.003 or 0.997, and someone in finance will ask why.
3. When you need calibrated prediction intervals for shares
This is where the gap is widest. Bottom-up forecasting with reconciliation gives you point estimates that (roughly) cohere. Getting coherent prediction intervals is harder. You need the joint distribution of all components, including their correlations, to produce intervals for the shares that respect the simplex constraint.
A Bayesian compositional model gives you this directly. The posterior predictive distribution lives on the simplex, so any credible interval you compute for a share is automatically bounded between 0 and 1, and the intervals for all shares are jointly consistent. Try getting that from independent ARIMA models with ad hoc reconciliation.
When to skip it
If your components are weakly correlated, you do not care about the shares (only the levels), and nobody downstream needs the parts to add up exactly, independent forecasting with reconciliation is simpler and probably sufficient. The setup cost of a compositional model is not zero. You need priors on the simplex, a sampler that handles the constraint, and stakeholders who understand what "Dirichlet" means or at least trust that it works.
The decision rule I use: if someone will divide your forecasts to compute a share and then make a decision based on that share, model the shares directly.