Adaptive forecasting of irregular demand processes

Abstract

The paper presents some comparative results from different approaches for solving the problem of forecasting irregularly spaced time series, including those representing irregular demand processes in inventory management or production planning problems. The best-known methods for that purpose are based on simple exponential smoothing of the time and magnitude of occurrence of demand. It is shown how supervised neural networks can be used as suitable filtering and forecasting models for many of those processes. In particular, we discuss the identification and estimation issues, as well as the limitations associated to the application of Gaussian radial or elliptical basis function networks.