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Generalised estimators for seasonal forecasting by combining grouping with shrinkage approaches

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Generalised estimators for seasonal forecasting by combining grouping with shrinkage approaches. / Zhang, Kui; Chen, Huijing; Boylan, John; Scarf, Philip.

In: Journal of Forecasting, Vol. 32, No. 2, 01.03.2013, p. 137-150.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Zhang, K, Chen, H, Boylan, J & Scarf, P 2013, 'Generalised estimators for seasonal forecasting by combining grouping with shrinkage approaches', Journal of Forecasting, vol. 32, no. 2, pp. 137-150. https://doi.org/10.1002/for.1254

APA

Vancouver

Author

Zhang, Kui ; Chen, Huijing ; Boylan, John ; Scarf, Philip. / Generalised estimators for seasonal forecasting by combining grouping with shrinkage approaches. In: Journal of Forecasting. 2013 ; Vol. 32, No. 2. pp. 137-150.

Bibtex

@article{9c79a59d5c924e0e8d882b33b3659952,
title = "Generalised estimators for seasonal forecasting by combining grouping with shrinkage approaches",
abstract = "In this paper, generalised estimators are proposed to estimate seasonal indices for certain forms of additive and mixed seasonality. The estimators combine one of two group seasonal indices methods—Dalhart's group method and Withycombe's group method—with a shrinkage method in different ways. Optimal shrinkage parameters are derived to maximise the performance of the estimators. Then, the generalised estimators, with the optimal shrinkage parameters, are evaluated based on forecasting accuracy. Moreover, the effects of three factors are examined, namely, the length of data history, variance of random components and the number of series. Finally, a simulation experiment is conducted to support the evaluation",
keywords = "forecasting, seasonality, grouping, shrinkage, generalised estimator",
author = "Kui Zhang and Huijing Chen and John Boylan and Philip Scarf",
year = "2013",
month = mar,
day = "1",
doi = "10.1002/for.1254",
language = "English",
volume = "32",
pages = "137--150",
journal = "Journal of Forecasting",
issn = "0277-6693",
publisher = "John Wiley and Sons Ltd",
number = "2",

}

RIS

TY - JOUR

T1 - Generalised estimators for seasonal forecasting by combining grouping with shrinkage approaches

AU - Zhang, Kui

AU - Chen, Huijing

AU - Boylan, John

AU - Scarf, Philip

PY - 2013/3/1

Y1 - 2013/3/1

N2 - In this paper, generalised estimators are proposed to estimate seasonal indices for certain forms of additive and mixed seasonality. The estimators combine one of two group seasonal indices methods—Dalhart's group method and Withycombe's group method—with a shrinkage method in different ways. Optimal shrinkage parameters are derived to maximise the performance of the estimators. Then, the generalised estimators, with the optimal shrinkage parameters, are evaluated based on forecasting accuracy. Moreover, the effects of three factors are examined, namely, the length of data history, variance of random components and the number of series. Finally, a simulation experiment is conducted to support the evaluation

AB - In this paper, generalised estimators are proposed to estimate seasonal indices for certain forms of additive and mixed seasonality. The estimators combine one of two group seasonal indices methods—Dalhart's group method and Withycombe's group method—with a shrinkage method in different ways. Optimal shrinkage parameters are derived to maximise the performance of the estimators. Then, the generalised estimators, with the optimal shrinkage parameters, are evaluated based on forecasting accuracy. Moreover, the effects of three factors are examined, namely, the length of data history, variance of random components and the number of series. Finally, a simulation experiment is conducted to support the evaluation

KW - forecasting

KW - seasonality

KW - grouping

KW - shrinkage

KW - generalised estimator

U2 - 10.1002/for.1254

DO - 10.1002/for.1254

M3 - Journal article

VL - 32

SP - 137

EP - 150

JO - Journal of Forecasting

JF - Journal of Forecasting

SN - 0277-6693

IS - 2

ER -