Cited by 3,201Forecasting Techniques and ApplicationsGrey System Theory ApplicationsStatistical and numerical algorithms

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The Combination of Forecasts

Bates, J. M., Granger, C. W. J.·1969·Journal of the Operational Research Society

DOI 10.1057/jors.1969.103

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Abstract

Two separate sets of forecasts of airline passenger data have been combined to form a composite set of forecasts. The main conclusion is that the composite set of forecasts can yield lower mean-square error than either of the original forecasts. Past errors of each of the original forecasts are used to determine the weights to attach to these two original forecasts in forming the combined forecasts, and different methods of deriving these weights are examined.

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Kendall's Advanced Theory of Statistics.
Aa, Stuart, A., & Ord, K. (1995) · Journal of the American Statistical Association · doi:10.2307/2291187 · Cited by 2,663
The Advanced Theory of Statistics
L., S., Kendall, M. G., & Stuart, A. (1963) · Population · doi:10.2307/1527192 · Cited by 1,840
The Precision of National Income Estimates
Stone, R., Champernowne, D. G., & Meade, J. E. (1942) · The Review of Economic Studies · doi:10.2307/2967664 · Cited by 232
New Methods of Quality Control
Barnard, G. A. (1963) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2982365 · Cited by 93
Some Statistical Aspects of Adaptive Optimization and Control
Box, G. E. P., & Jenkins, G. M. (1992) · Springer series in statistics · doi:10.1007/978-1-4612-4380-9_29 · Cited by 91
Short-Term Sales Forecasting
Harrison, P. J. (1965) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.2307/2985338 · Cited by 73
MODELS FOR FORECASTING SEASONAL AND NON-SEASONAL TIME SERIES.
Box, G. E. P., Jenkins, G. M., & Bacon, D. W. (1967) · Cited by 34

Show all 7 references

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Authors: Bates, J. M., & Granger, C. W. J.
Affiliations: University of Nottingham (GB)
Venue: Journal of the Operational Research Society
Publisher: Informa UK Limited
Published: 1969-12
Volume: 20
Issue: 4
Pages: 451-468
ISSN: 0160-5682 (ISSN-L)
1476-9360
DOI: 10.1057/jors.1969.103
OpenAlex: W1986528915
Cited by: 3,201
References: 7
Citations by year: 2026: 148 · 2025: 107 · 2024: 127 · 2023: 169 · 2022: 159 · 2021: 151 · 2020: 167 · 2019: 177 · 2018: 132 · 2017: 115 · 2016: 141 · 2015: 161 · 2014: 119 · 2013: 134 · 2012: 131
FWCI: 0.85
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (unspecified)
Locations: Journal of the Operational Research Society (publishedVersion)
Topics: Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
Social Sciences › Decision Sciences › Management Science and Operations Research › Grey System Theory Applications
Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
Keywords: forecast combination, forecast weights, mean squared error, forecast error, forecast accuracy

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The Combination of Forecasts

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