An Interpretable Structural-Break Forecasting Framework for Limited-Sample Quarterly Revenue Prediction: Evidence from Coca-Cola

Forecasting quarterly revenue during structural breaks remains challenging, particularly when only limited historical data are available and interpretability is required for business decision-making. This study proposes an interpretable forecasting framework that integrates formal structural break detection, explicit break-specification strategies, and recursive forecast evaluation for quarterly revenue prediction under limited-data conditions. Using 64 quarterly observations (2010–2025) of Coca-Cola revenue, we implement a recursive forecasting experiment where models are estimated using only past data at each forecast origin—eliminating look-ahead bias. We compare polynomial regression against classical time series methods (ARIMA, SARIMA, Prophet) and machine learning models (Random Forest, XGBoost, LightGBM, CatBoost), with ML models receiving autoregressive features (lags 1, 2, 4, moving averages) and calendar features for a fair comparison. The Bai–Perron test is applied within an expanding-window forecasting protocol to evaluate forecasting under sequential information availability. A cubic polynomial with a level-shift dummy achieves R2 and MAE = USD 0.29 B when the break date is known in advance (ex post benchmark). In the more realistic expanding-window protocol, where the break is detected using only past data, the MAE is USD 0.31 B. Under the specific limited-sample Coca-Cola forecasting setting considered, these results are competitive with classical benchmarks (ARIMA: MAE = 1.34 B; SARIMA: MAE = 1.18 B) and machine learning methods (Random Forest: MAE = 0.38 B; XGBoost: MAE = 0.41 B) while remaining fully interpretable. The framework provides explicit coefficient estimates with direct business meanings, enabling stakeholders to understand and act on forecasts. However, validation on PepsiCo data illustrates limited transferability without recalibration, indicating that the framework should not be assumed to generalize broadly.