Selección y utilización de niveles de desagregación adecuados en pronósticos de series temporales: caso de estudio en una empresa de suscripción utilizando el proceso analítico jerárquico

Hierarchical aggregation/disaggregation of time series in order to make forecasts is a frequent challenge in business and econometric scenarios. This work presents a novel approach for selecting an adequate time series disaggregation level as a starting point for making forecasts. The methodology combines qualitative criteria - such as business resources and decision environment - and quantitative criteria - such as information quality and forecastability - in a multicriteria decision making task which is addressed through the analytic hierarchy process (AHP) technique. Results from a study case in a subscription business model company show the usefulness of combining AHP and time series forecasting techniques and the importance of multicriteria decision-making in the task of selecting an adequate aggregation/disaggregation level.