Using high-frequency data and time series models to Improve yield management

High-frequency (less than monthly) time series data provide valuable information for designing the adequate yield policy of the organisation. However, it is not easy to extract this information from raw data; although the evolution of the series is usually induced by stable patterns of behaviour of the economic agents, these patterns are so complex that simple smoothing techniques or subjective forecasting cannot consider all underlying factors. In this paper, we discuss time series models as a tool for carrying out a full and efficient analysis. The main ideas are illustrated with an application to Spanish daily electricity consumption.This paper is based upon work supported by the Spanish DGICYT, projects PB93-0236 (both authors) and PB93-0653 (Cancelo).Publicad