A Bayesian Dirichlet Auto-Regressive Moving Average Model for Forecasting Lead Times

Abstract: Lead time data is compositional data found frequently in the hospitality industry. Hospitality businesses earn fees each day, however these fees cannot be recognized until later. For business purposes, it is important to understand and forecast the distribution of future fees for the allocation of resources, for business planning, and for staffing. Motivated by 5 years of daily fees data, we propose a new class of Bayesian time series models, a Bayesian Dirichlet Auto-Regressive Moving Average (B-DARMA) model for compositional time series, modeling the proportion of future fees that will be recognized in 11 consecutive 30 day windows and 1 last consecutive 35 day window. Each day's compositional datum is modeled as Dirichlet distributed given the mean and a scale parameter. The mean is modeled with a Vector Autoregressive Moving Average process after transforming with an additive log ratio link function and depends on previous compositional data, previous compositional parameters and daily covariates. The B-DARMA model offers solutions to data analyses of large compositional vectors and short or long time series, offers efficiency gains through choice of priors, provides interpretable parameters for inference, and makes reasonable forecasts.