Home > Research > Publications & Outputs > Cross-temporal coherent forecasts for Australia...

Electronic data

  • Kourentzes_2019_Cross_temporal_coherent_forecasts__the_case_of_Australian_tourism_flows

    Rights statement: This is the author’s version of a work that was accepted for publication in Annals of Tourism Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Annals of Tourism Research 75, 2019 DOI: 10.1016/j.annals.2019.02.001

    Accepted author manuscript, 443 KB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

View graph of relations

Cross-temporal coherent forecasts for Australian tourism

Research output: Contribution to journalJournal articlepeer-review

Published

Standard

Cross-temporal coherent forecasts for Australian tourism. / Kourentzes, Nikolaos; Athanasopoulos, George.

In: Annals of Tourism Research, Vol. 75, 31.03.2019, p. 393-409.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Kourentzes, N & Athanasopoulos, G 2019, 'Cross-temporal coherent forecasts for Australian tourism', Annals of Tourism Research, vol. 75, pp. 393-409. https://doi.org/10.1016/j.annals.2019.02.001

APA

Vancouver

Author

Kourentzes, Nikolaos ; Athanasopoulos, George. / Cross-temporal coherent forecasts for Australian tourism. In: Annals of Tourism Research. 2019 ; Vol. 75. pp. 393-409.

Bibtex

@article{76ea5aebd78c41f3b395f4750a4f935c,
title = "Cross-temporal coherent forecasts for Australian tourism",
abstract = "Key to ensuring a successful tourism sector is timely policy making and detailed planning. National policy formulation and strategic planning requires long-term forecasts at an aggregate level, while regional operational decisions require short-term forecasts, relevant to local tourism operators. For aligned decisions at all levels, supporting forecasts must be `coherent', that is they should add up appropriately, across relevant demarcations (e.g., geographical divisions or market segments) and also across time. We propose an approach for generating coherent forecasts across both cross-sections and planning horizons for Australia. This results in significant improvements in forecast accuracy with substantial decision making benefits. Coherent forecasts help break intra- and inter-organisational information and planning silos, in a data driven fashion, blending information from different sources.",
keywords = "Forecasting, Cross-sectional aggregation, Temporal aggregation, Forecast combination, Spatial correlations",
author = "Nikolaos Kourentzes and George Athanasopoulos",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Annals of Tourism Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Annals of Tourism Research 75, 2019 DOI: 10.1016/j.annals.2019.02.001",
year = "2019",
month = mar,
day = "31",
doi = "10.1016/j.annals.2019.02.001",
language = "English",
volume = "75",
pages = "393--409",
journal = "Annals of Tourism Research",
issn = "0160-7383",
publisher = "Elsevier Ltd",

}

RIS

TY - JOUR

T1 - Cross-temporal coherent forecasts for Australian tourism

AU - Kourentzes, Nikolaos

AU - Athanasopoulos, George

N1 - This is the author’s version of a work that was accepted for publication in Annals of Tourism Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Annals of Tourism Research 75, 2019 DOI: 10.1016/j.annals.2019.02.001

PY - 2019/3/31

Y1 - 2019/3/31

N2 - Key to ensuring a successful tourism sector is timely policy making and detailed planning. National policy formulation and strategic planning requires long-term forecasts at an aggregate level, while regional operational decisions require short-term forecasts, relevant to local tourism operators. For aligned decisions at all levels, supporting forecasts must be `coherent', that is they should add up appropriately, across relevant demarcations (e.g., geographical divisions or market segments) and also across time. We propose an approach for generating coherent forecasts across both cross-sections and planning horizons for Australia. This results in significant improvements in forecast accuracy with substantial decision making benefits. Coherent forecasts help break intra- and inter-organisational information and planning silos, in a data driven fashion, blending information from different sources.

AB - Key to ensuring a successful tourism sector is timely policy making and detailed planning. National policy formulation and strategic planning requires long-term forecasts at an aggregate level, while regional operational decisions require short-term forecasts, relevant to local tourism operators. For aligned decisions at all levels, supporting forecasts must be `coherent', that is they should add up appropriately, across relevant demarcations (e.g., geographical divisions or market segments) and also across time. We propose an approach for generating coherent forecasts across both cross-sections and planning horizons for Australia. This results in significant improvements in forecast accuracy with substantial decision making benefits. Coherent forecasts help break intra- and inter-organisational information and planning silos, in a data driven fashion, blending information from different sources.

KW - Forecasting

KW - Cross-sectional aggregation

KW - Temporal aggregation

KW - Forecast combination

KW - Spatial correlations

U2 - 10.1016/j.annals.2019.02.001

DO - 10.1016/j.annals.2019.02.001

M3 - Journal article

VL - 75

SP - 393

EP - 409

JO - Annals of Tourism Research

JF - Annals of Tourism Research

SN - 0160-7383

ER -