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Activity Number: 643
Type: Contributed
Date/Time: Thursday, August 13, 2015 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #316671
Title: Enterprise Business Metric Forecasting
Author(s): Beatriz Etchegaray Garcia* and Yasuo Amemiya
Companies: IBM Research and IBM Research
Keywords: hierarchical forecasting ; multivariate time series ; enterprise structure

Regularly issued forecasts of various business metrics, such as revenue, cost, and event occurrences, play a key role in tracking and managing business performance. Some forecasts are for next several time periods, while others target specific term summaries such as quarterly totals. In an enterprise business, processes are often organized in a hierarchical and high-dimensional cube structure based on features such as product offerings or geography. Typically, forecasts need to be issued for every cell of the cube and must satisfy certain consistency relationships over the different dimensions. Forecasting for a hierarchical structure of time series is challenging because various components at different levels of the hierarchy can interact in a complex manner. A large number of time-dependent covariates are usually available, although the measurement periods may vary, and missing or unstable measurements are expected in some cells. Desirable forecast properties include reasonable accuracy at various levels or slices of a cube, and stability over time periods. In this talk we present actual enterprise forecasting problems and their challenges. We describe the development and implementation of methods for an operational solution effectively used by business.

Authors who are presenting talks have a * after their name.

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