An improved practical approach to forecasting exceptional growth in research

Abstract

Abstract The accurate forecasting of exceptional growth in research areas has been an extremely difficult problem to solve. In a previous study we introduced an approach to forecasting which research clusters in a global model of the scientific literature would have an annual growth rate of 8% annually over a 3-year period. In this study we (a) introduce a much more robust method of creating and updating global models of research, (b) introduce new indicators based on author publication patterns, (c) test a much larger set (81) of indicators to forecast exceptional growth, and (d) expand the forecast horizon from 3 to 4 years. Forecast accuracy increased dramatically (threat score increased from 20 to 32) from our previous study. Most of this gain is surprisingly due to the advances in model robustness rather than the indicators used for forecasting. We also provide evidence that most indicators (including popular network indicators) do not improve the ability to forecast growth in research above the baseline provided by indicators associated with the vitality of a research cluster.

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