Trade-Offs in Multi-Horizon Forecasting Systems

Accuracy vs Horizon Range

Extending forecast horizons reduces accuracy at all horizons. Models must allocate capacity between near-term precision and long-term coverage. A 90-day model will underperform a 30-day model at the 7-day horizon because it learns less precise short-term patterns. Match horizon range to actual business needs—do not forecast further than decisions require.

Design Principle: If business needs short and long horizons, consider separate models. A 7-day high-precision model for operations plus a 90-day lower-precision model for planning often outperforms a single compromise model.

Computational Cost vs Freshness

More frequent forecast updates improve freshness but multiply compute costs. Daily updates for millions of series across 30+ horizons is expensive. Tier refresh rates: high-value series update hourly, medium-value daily, low-value weekly. Not all series deserve the same freshness.

Model Complexity vs Interpretability

Deep learning models (Transformers, LSTMs) achieve state-of-the-art accuracy but are black boxes. Statistical models (ARIMA, ETS) are interpretable—you can explain why forecasts changed. In regulated environments or when stakeholder trust matters, interpretability may outweigh accuracy gains.

Hybrid Solution: Use DL for point forecasts, add interpretable components (decomposition into trend/seasonal/residual) for explainability. Stakeholders see familiar patterns; model benefits from DL accuracy.

Global vs Local Models

Global models (one model, all series) transfer learning across series but may underfit unique patterns. Local models (per-series) capture unique patterns but cannot generalize. Hybrid: global model with series-specific fine-tuning or local adjustment layers.