Philosophy & Technology

, Volume 31, Issue 4, pp 557–569 | Cite as

Forecasting in Light of Big Data

  • Hykel Hosni
  • Angelo Vulpiani
Research Article


Predicting the future state of a system has always been a natural motivation for science and practical applications. Such a topic, beyond its obvious technical and societal relevance, is also interesting from a conceptual point of view. This owes to the fact that forecasting lends itself to two equally radical, yet opposite methodologies. A reductionist one, based on first principles, and the naïve-inductivist one, based only on data. This latter view has recently gained some attention in response to the availability of unprecedented amounts of data and increasingly sophisticated algorithmic analytic techniques. The purpose of this note is to assess critically the role of big data in reshaping the key aspects of forecasting and in particular the claim that bigger data leads to better predictions. Drawing on the representative example of weather forecasts we argue that this is not generally the case. We conclude by suggesting that a clever and context-dependent compromise between modelling and quantitative analysis stands out as the best forecasting strategy, as anticipated nearly a century ago by Richardson and von Neumann.


Forecasting Big data Epistemology 


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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Dipartimento di FilosofiaUniversità degli Studi di MilanoMilanoItaly
  2. 2.Dipartimento di FisicaUniversità degli Studi di Roma SapienzaRomaItaly
  3. 3.Centro Linceo Inderdisciplinare “Beniamino Segre”Accademia dei LinceiRomaItaly

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