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Forecast horizon aggregation in integer autoregressive moving average (INARMA) models

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Forecast horizon aggregation in integer autoregressive moving average (INARMA) models. / Mohammadipour, Maryam; Boylan, John.

In: Omega: The International Journal of Management Science, Vol. 40, No. 6, 01.12.2012, p. 703-712.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Mohammadipour, M & Boylan, J 2012, 'Forecast horizon aggregation in integer autoregressive moving average (INARMA) models', Omega: The International Journal of Management Science, vol. 40, no. 6, pp. 703-712. https://doi.org/10.1016/j.omega.2011.08.008

APA

Mohammadipour, M., & Boylan, J. (2012). Forecast horizon aggregation in integer autoregressive moving average (INARMA) models. Omega: The International Journal of Management Science, 40(6), 703-712. https://doi.org/10.1016/j.omega.2011.08.008

Vancouver

Mohammadipour M, Boylan J. Forecast horizon aggregation in integer autoregressive moving average (INARMA) models. Omega: The International Journal of Management Science. 2012 Dec 1;40(6):703-712. https://doi.org/10.1016/j.omega.2011.08.008

Author

Mohammadipour, Maryam ; Boylan, John. / Forecast horizon aggregation in integer autoregressive moving average (INARMA) models. In: Omega: The International Journal of Management Science. 2012 ; Vol. 40, No. 6. pp. 703-712.

Bibtex

@article{89a1e8fcca364d2585f59e2574d6ba42,
title = "Forecast horizon aggregation in integer autoregressive moving average (INARMA) models",
abstract = "This paper addresses aggregation in integer autoregressive moving average (INARMA) models. Although aggregation in continuous-valued time series has been widely discussed, the same is not true for integer-valued time series. Forecast horizon aggregation is addressed in this paper. It is shown that the overlapping forecast horizon aggregation of an INARMA process results in an INARMA process. The conditional expected value of the aggregated process is also derived for use in forecasting. A simulation experiment is conducted to assess the accuracy of the forecasts produced by the aggregation method and to compare it to the accuracy of cumulative h-step ahead forecasts over the forecasting horizon. The results of an empirical analysis are also provided.",
keywords = "Discrete time series, INARMA model, Temporal aggregation, Cross-sectional aggregation, Forecast horizon, Yule-Walker estimation",
author = "Maryam Mohammadipour and John Boylan",
year = "2012",
month = dec,
day = "1",
doi = "10.1016/j.omega.2011.08.008",
language = "English",
volume = "40",
pages = "703--712",
journal = "Omega: The International Journal of Management Science",
issn = "0305-0483",
publisher = "Elsevier BV",
number = "6",

}

RIS

TY - JOUR

T1 - Forecast horizon aggregation in integer autoregressive moving average (INARMA) models

AU - Mohammadipour, Maryam

AU - Boylan, John

PY - 2012/12/1

Y1 - 2012/12/1

N2 - This paper addresses aggregation in integer autoregressive moving average (INARMA) models. Although aggregation in continuous-valued time series has been widely discussed, the same is not true for integer-valued time series. Forecast horizon aggregation is addressed in this paper. It is shown that the overlapping forecast horizon aggregation of an INARMA process results in an INARMA process. The conditional expected value of the aggregated process is also derived for use in forecasting. A simulation experiment is conducted to assess the accuracy of the forecasts produced by the aggregation method and to compare it to the accuracy of cumulative h-step ahead forecasts over the forecasting horizon. The results of an empirical analysis are also provided.

AB - This paper addresses aggregation in integer autoregressive moving average (INARMA) models. Although aggregation in continuous-valued time series has been widely discussed, the same is not true for integer-valued time series. Forecast horizon aggregation is addressed in this paper. It is shown that the overlapping forecast horizon aggregation of an INARMA process results in an INARMA process. The conditional expected value of the aggregated process is also derived for use in forecasting. A simulation experiment is conducted to assess the accuracy of the forecasts produced by the aggregation method and to compare it to the accuracy of cumulative h-step ahead forecasts over the forecasting horizon. The results of an empirical analysis are also provided.

KW - Discrete time series

KW - INARMA model

KW - Temporal aggregation

KW - Cross-sectional aggregation

KW - Forecast horizon

KW - Yule-Walker estimation

U2 - 10.1016/j.omega.2011.08.008

DO - 10.1016/j.omega.2011.08.008

M3 - Journal article

VL - 40

SP - 703

EP - 712

JO - Omega: The International Journal of Management Science

JF - Omega: The International Journal of Management Science

SN - 0305-0483

IS - 6

ER -