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Improving Forecasting via Multiple Temporal Aggregation
Lancaster University, United Kingdom.
Lancaster University, United Kingdom.ORCID iD: 0000-0003-0211-5218
2014 (English)In: Foresight: The International Journal of Applied Forecasting, ISSN 1555-9068, no 34, p. 12-17Article in journal (Refereed) Published
Abstract [en]

In most business forecasting applications, the decision-making need we have directs the frequency of the data we collect (monthly, weekly, etc.) and use for forecasting. In this article, Fotios and Nikolaos introduce an approach that combines forecasts generated by modeling the different frequencies (levels of temporal aggregation). Their technique augments our information about the data used for forecasting and, as such, can result in more accurate forecasts. It also automatically reconciles the forecasts at different levels.

Place, publisher, year, edition, pages
International Institute of Forecasters , 2014. no 34, p. 12-17
National Category
Probability Theory and Statistics Economics
Identifiers
URN: urn:nbn:se:his:diva-18281OAI: oai:DiVA.org:his-18281DiVA, id: diva2:1411595
Available from: 2020-03-04 Created: 2020-03-04 Last updated: 2020-03-04Bibliographically approved

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Kourentzes, Nikolaos

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