Forecasting time series with missing data using Holtʹs model

This paper deals with the prediction of time series with missing data using an alternative formulation for Holtʹs model with additive errors. This formulation simplifies both the calculus of maximum likelihood estimators of all the unknowns in the model and the calculus of point forecasts. In the presence of missing data, the EM algorithm is used to obtain maximum likelihood estimates and point forecasts. Based on this application we propose a leave-one-out algorithm for the data transformation selection problem which allows us to analyse Holtʹs model with multiplicative errors. Some numerical results show the performance of these procedures for obtaining robust forecasts.