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Optimality and robustness of combinations of moving averages

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Optimality and robustness of combinations of moving averages. / Boylan, John; Johnston, F. R.

In: Journal of the Operational Research Society, Vol. 54, No. 1, 01.01.2003, p. 109-115.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Boylan, J & Johnston, FR 2003, 'Optimality and robustness of combinations of moving averages', Journal of the Operational Research Society, vol. 54, no. 1, pp. 109-115. https://doi.org/10.1057/palgrave.jors.2601472

APA

Boylan, J., & Johnston, F. R. (2003). Optimality and robustness of combinations of moving averages. Journal of the Operational Research Society, 54(1), 109-115. https://doi.org/10.1057/palgrave.jors.2601472

Vancouver

Boylan J, Johnston FR. Optimality and robustness of combinations of moving averages. Journal of the Operational Research Society. 2003 Jan 1;54(1):109-115. doi: 10.1057/palgrave.jors.2601472

Author

Boylan, John ; Johnston, F. R. / Optimality and robustness of combinations of moving averages. In: Journal of the Operational Research Society. 2003 ; Vol. 54, No. 1. pp. 109-115.

Bibtex

@article{0507f63ef1f24151a64e868f1735c866,
title = "Optimality and robustness of combinations of moving averages",
abstract = "A combination of moving averages has been shown previously to be more accurate than simple moving averages, under certain conditions, and to be more robust to non-optimal parameter specification. However, the use of the method depends on specification of three parameters: length of greater moving average, length of shorter moving average, and the weighting given to the former. In this paper, expressions are derived for the optimal values of the three parameters, under the conditions of a steady state model. These expressions reduce a three-parameter search to a single-parameter search. An expression is given for the variance of the sampling error of the optimal combination of moving averages and this is shown to be marginally greater than that for exponentially weighted moving averages (EWMA). Similar expressions for optimal parameters and the resultant variance are derived for equally weighted combinations. The sampling variance of the mean of such combinations is shown to be almost identical to the optimal general combination, thus simplifying the use of combinations further. It is demonstrated that equal weight combinations are more robust than EWMA to noise to signal ratios lower than expected, but less robust to noise to signal ratios higher than expected.",
author = "John Boylan and Johnston, {F. R.}",
year = "2003",
month = jan,
day = "1",
doi = "10.1057/palgrave.jors.2601472",
language = "English",
volume = "54",
pages = "109--115",
journal = "Journal of the Operational Research Society",
issn = "0160-5682",
publisher = "Taylor and Francis Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - Optimality and robustness of combinations of moving averages

AU - Boylan, John

AU - Johnston, F. R.

PY - 2003/1/1

Y1 - 2003/1/1

N2 - A combination of moving averages has been shown previously to be more accurate than simple moving averages, under certain conditions, and to be more robust to non-optimal parameter specification. However, the use of the method depends on specification of three parameters: length of greater moving average, length of shorter moving average, and the weighting given to the former. In this paper, expressions are derived for the optimal values of the three parameters, under the conditions of a steady state model. These expressions reduce a three-parameter search to a single-parameter search. An expression is given for the variance of the sampling error of the optimal combination of moving averages and this is shown to be marginally greater than that for exponentially weighted moving averages (EWMA). Similar expressions for optimal parameters and the resultant variance are derived for equally weighted combinations. The sampling variance of the mean of such combinations is shown to be almost identical to the optimal general combination, thus simplifying the use of combinations further. It is demonstrated that equal weight combinations are more robust than EWMA to noise to signal ratios lower than expected, but less robust to noise to signal ratios higher than expected.

AB - A combination of moving averages has been shown previously to be more accurate than simple moving averages, under certain conditions, and to be more robust to non-optimal parameter specification. However, the use of the method depends on specification of three parameters: length of greater moving average, length of shorter moving average, and the weighting given to the former. In this paper, expressions are derived for the optimal values of the three parameters, under the conditions of a steady state model. These expressions reduce a three-parameter search to a single-parameter search. An expression is given for the variance of the sampling error of the optimal combination of moving averages and this is shown to be marginally greater than that for exponentially weighted moving averages (EWMA). Similar expressions for optimal parameters and the resultant variance are derived for equally weighted combinations. The sampling variance of the mean of such combinations is shown to be almost identical to the optimal general combination, thus simplifying the use of combinations further. It is demonstrated that equal weight combinations are more robust than EWMA to noise to signal ratios lower than expected, but less robust to noise to signal ratios higher than expected.

U2 - 10.1057/palgrave.jors.2601472

DO - 10.1057/palgrave.jors.2601472

M3 - Journal article

VL - 54

SP - 109

EP - 115

JO - Journal of the Operational Research Society

JF - Journal of the Operational Research Society

SN - 0160-5682

IS - 1

ER -