Obtaining Quantitative Predictions from Monotone Relationships

: Tasks such as forecasting, diagnosis, and planning frequently require quantitative predictions . Typically, quantitative predictions are obtained by characterizing a system in terms of algebraic relationships and then using these relationships to compute quantitative predictions from numerical data . For real-life systems, such as mainframe operating systems, an algebraic characterization is often difficult, if not intractable. This paper proposes a statistical approach to obtaining quantitative predictions from monotone relationships -- non-parametric interpolative-prediction for monotone functions (NIMF). NIMF uses monotone relationships to search historical data for bounds that provide a desired level of statistical confidence . We evaluate NIMF by comparing its predictions to those of linear least-squares regression (a widely-used statistical technique that requires specifying algebraic relationships) for memory contention in an IBM computer system . We find that NIMF consistentl...

• Type: Conference
• Year: 1990
• Where: AAAI
• Authors: Joseph L. Hellerstein
• Keywords: AAAI 1990, Algebraic Relationship, Intelligent Agents, Monotone Relationships, Quantitative Predictions