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Forecasting: theory and practice
School of Management, University of Bath, United Kingdom.
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningsmiljön Informationsteknologi. Centre for Marketing Analytics and Forecasting, Lancaster University Management School, Lancaster University, United Kingdom. (Skövde Artificial Intelligence Lab (SAIL))ORCID-id: 0000-0003-0211-5218
House of Energy Markets and Finance, University of Duisburg–Essen, Germany.
Rekke forfattare: 802022 (engelsk)Inngår i: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 38, nr 3, s. 705-871Artikkel, forskningsoversikt (Fagfellevurdert) Published
Abstract [en]

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. 

sted, utgiver, år, opplag, sider
Elsevier, 2022. Vol. 38, nr 3, s. 705-871
Emneord [en]
Applications, Encyclopedia, Methods, Prediction, Principles, Review, Time series
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
URN: urn:nbn:se:his:diva-20867DOI: 10.1016/j.ijforecast.2021.11.001ISI: 000836807400001Scopus ID: 2-s2.0-85123082516OAI: oai:DiVA.org:his-20867DiVA, id: diva2:1632482
Merknad

CC BY 4.0

© 2021 The Author(s)

Corresponding author: E-mail addresses: f.petropoulos@bath.ac.uk, fotios@bath.edu (F. Petropoulos).

Available online 20 January 2022

Tilgjengelig fra: 2022-01-27 Laget: 2022-01-27 Sist oppdatert: 2025-09-29bibliografisk kontrollert

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