Principles of Forecasting

Volume 30 of the series International Series in Operations Research & Management Science pp 389-403

Judgmental Time-Series Forecasting Using Domain Knowledge

  • Richard WebbyAffiliated withTelecordia Technologies
  • , Marcus O’ConnorAffiliated withSchool of Information Systems, University of New South Wales
  • , Michael LawrenceAffiliated withSchool of Information Systems, University of New South Wales

* Final gross prices may vary according to local VAT.

Get Access


This chapter concerns principles regarding when and how to use judgment in time-series forecasting with domain knowledge. The evidence suggests that the reliability of domain knowledge is critical, and that judgment is essential when dealing with “soft” information. However judgment suffers from biases and inefficiencies when dealing with domain knowledge. We suggest two sets of principles for dealing with domain knowledge—when to use it and how to use it. Domain knowledge should be used when there is a large amount of relevant information, when experts are deemed to possess it, and when the experts do not appear to have predetermined agendas for the final forecast or the forecast setting process. Forecasters should select only the most important causal information, adjust initial estimates boldly in the light of new domain knowledge, and use decomposition strategies to integrate domain knowledge into the forecast.


Contextual information domain knowledge judgmental decomposition judgmental forecasting time-series forecasting