Approaches to forecasting demands for library network services

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Abstract

The problem of forecasting monthly demands for library network services is considered, especially in terms of using forecasts as inputs to policy analysis models and in terms of the use of forecasts as an aid to budgeting and staffing decisions. Forecasting methods considered include Box-Jenkins time-series methodology, adaptive filtering, and linear regression. Using demand data from the Illinois Library and Information Network for 1971–1978, it is shown that fading-memory regression is the most appropriate method, in terms of both accuracy and ease of use.

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