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Improving forecasting via multiple temporal aggregation

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Improving forecasting via multiple temporal aggregation. / Petropoulos, Fotios; Kourentzes, Nikos.

In: Foresight: The International Journal of Applied Forecasting, Vol. 2014, No. 34, 2014, p. 12-17.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Petropoulos, F & Kourentzes, N 2014, 'Improving forecasting via multiple temporal aggregation', Foresight: The International Journal of Applied Forecasting, vol. 2014, no. 34, pp. 12-17. <http://ideas.repec.org/a/for/ijafaa/y2014i34p12-17.html>

APA

Vancouver

Petropoulos F, Kourentzes N. Improving forecasting via multiple temporal aggregation. Foresight: The International Journal of Applied Forecasting. 2014;2014(34):12-17.

Author

Petropoulos, Fotios ; Kourentzes, Nikos. / Improving forecasting via multiple temporal aggregation. In: Foresight: The International Journal of Applied Forecasting. 2014 ; Vol. 2014, No. 34. pp. 12-17.

Bibtex

@article{c813b50515e647598083a60e016fe760,
title = "Improving forecasting via multiple temporal aggregation",
abstract = "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 we 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.",
author = "Fotios Petropoulos and Nikos Kourentzes",
year = "2014",
language = "English",
volume = "2014",
pages = "12--17",
journal = "Foresight: The International Journal of Applied Forecasting",
issn = "1555-9068",
publisher = "International Institute of Forecasters",
number = "34",

}

RIS

TY - JOUR

T1 - Improving forecasting via multiple temporal aggregation

AU - Petropoulos, Fotios

AU - Kourentzes, Nikos

PY - 2014

Y1 - 2014

N2 - 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 we 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.

AB - 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 we 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.

M3 - Journal article

VL - 2014

SP - 12

EP - 17

JO - Foresight: The International Journal of Applied Forecasting

JF - Foresight: The International Journal of Applied Forecasting

SN - 1555-9068

IS - 34

ER -