multihorizon

Multihorizon is a conceptual framework and methodological approach for modeling, forecasting, and decision-making that explicitly considers multiple time horizons simultaneously. The idea is to recognize that processes unfold over different time scales and that outcomes on one horizon can affect or be affected by outcomes on another. The term is used across disciplines to describe approaches that integrate near-term dynamics with longer-term trends within a unified modeling and planning pipeline. There is no universally accepted definition, and usage varies by field.

Core concepts include horizon segmentation (defining short-, medium-, and long-term horizons), cross-horizon coupling (representing dependencies across horizons), multi-horizon optimization (decision problems that optimize objectives over several horizons), and uncertainty propagation (how uncertainty at one horizon affects others). Methods commonly combine hierarchical or multiscale modeling, multi-step forecasting, and scenario analysis. Techniques include hierarchical time series forecasting, rolling-origin evaluation, and approaches from model predictive control and machine learning designed for multi-scale data.

Applications span economics and finance, energy and climate systems, supply chain and operations planning, urban planning, and public policy. In practice, multihorizon methods use a mix of data sources, scenario narratives, and simulation to compare strategies across horizons and assess trade-offs between short-term performance and long-term resilience or sustainability.

Challenges include aligning data across horizons, choosing horizon delineations and weights, managing computational complexity, and evaluating performance across multiple horizons. Interpretability and clear communication to stakeholders are important considerations in deploying multihorizon analyses. See also hierarchical forecasting, multiscale modeling, model predictive control, and uncertainty quantification.