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Should we sample a time series more frequently? decision support via multirate spectrum estimation

Research output: Contribution to journalArticle

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Should we sample a time series more frequently? decision support via multirate spectrum estimation. / Nason, Guy P.; Powell, Benedict James; Elliott, Duncan; Smith, Paul A.

In: Journal of the Royal Statistical Society: Series A (Statistics in Society), Vol. 180, No. 2, 18.12.2016, p. 353-407.

Research output: Contribution to journalArticle

Harvard

Nason, GP, Powell, BJ, Elliott, D & Smith, PA 2016, 'Should we sample a time series more frequently? decision support via multirate spectrum estimation', Journal of the Royal Statistical Society: Series A (Statistics in Society), vol. 180, no. 2, pp. 353-407. https://doi.org/10.1111/rssa.12210

APA

Nason, G. P., Powell, B. J., Elliott, D., & Smith, P. A. (2016). Should we sample a time series more frequently? decision support via multirate spectrum estimation. Journal of the Royal Statistical Society: Series A (Statistics in Society), 180(2), 353-407. https://doi.org/10.1111/rssa.12210

Vancouver

Nason GP, Powell BJ, Elliott D, Smith PA. Should we sample a time series more frequently? decision support via multirate spectrum estimation. Journal of the Royal Statistical Society: Series A (Statistics in Society). 2016 Dec 18;180(2):353-407. https://doi.org/10.1111/rssa.12210

Author

Nason, Guy P. ; Powell, Benedict James ; Elliott, Duncan ; Smith, Paul A. / Should we sample a time series more frequently? decision support via multirate spectrum estimation. In: Journal of the Royal Statistical Society: Series A (Statistics in Society). 2016 ; Vol. 180, No. 2. pp. 353-407.

Bibtex - Download

@article{e0798a6c708b47e18a306eaa60752f66,
title = "Should we sample a time series more frequently?: decision support via multirate spectrum estimation",
abstract = "Suppose that we have a historical time series with samples taken at a slow rate, e.g. quarterly. The paper proposes a new method to answer the question: is it worth sampling the series at a faster rate, e.g. monthly? Our contention is that classical time series methods are designed to analyse a series at a single and given sampling rate with the consequence that analysts are not often encouraged to think carefully about what an appropriate sampling rate might be. To answer the sampling rate question we propose a novel Bayesian method that incorporates the historical series, cost information and small amounts of pilot data sampled at the faster rate. The heart of our method is a new Bayesian spectral estimation technique that is capable of coherently using data sampled at multiple rates and is demonstrated to have superior practical performance compared with alternatives. Additionally, we introduce a method for hindcasting historical data at the faster rate. A freeware R package, regspec, is available that implements our methods. We illustrate our work by using official statistics time series including the UK consumer price index and counts of UK residents travelling abroad, but our methods are general and apply to any situation where time series data are collected.",
keywords = "Aliasing, Bayesian statistics, Multirate, Spectrum estimation, Time series",
author = "Nason, {Guy P.} and Powell, {Benedict James} and Duncan Elliott and Smith, {Paul A.}",
year = "2016",
month = "12",
day = "18",
doi = "10.1111/rssa.12210",
language = "English",
volume = "180",
pages = "353--407",
journal = "Journal of the Royal Statistical Society: Series A (Statistics in Society)",
issn = "0964-1998",
publisher = "Wiley",
number = "2",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Should we sample a time series more frequently?

T2 - decision support via multirate spectrum estimation

AU - Nason, Guy P.

AU - Powell, Benedict James

AU - Elliott, Duncan

AU - Smith, Paul A.

PY - 2016/12/18

Y1 - 2016/12/18

N2 - Suppose that we have a historical time series with samples taken at a slow rate, e.g. quarterly. The paper proposes a new method to answer the question: is it worth sampling the series at a faster rate, e.g. monthly? Our contention is that classical time series methods are designed to analyse a series at a single and given sampling rate with the consequence that analysts are not often encouraged to think carefully about what an appropriate sampling rate might be. To answer the sampling rate question we propose a novel Bayesian method that incorporates the historical series, cost information and small amounts of pilot data sampled at the faster rate. The heart of our method is a new Bayesian spectral estimation technique that is capable of coherently using data sampled at multiple rates and is demonstrated to have superior practical performance compared with alternatives. Additionally, we introduce a method for hindcasting historical data at the faster rate. A freeware R package, regspec, is available that implements our methods. We illustrate our work by using official statistics time series including the UK consumer price index and counts of UK residents travelling abroad, but our methods are general and apply to any situation where time series data are collected.

AB - Suppose that we have a historical time series with samples taken at a slow rate, e.g. quarterly. The paper proposes a new method to answer the question: is it worth sampling the series at a faster rate, e.g. monthly? Our contention is that classical time series methods are designed to analyse a series at a single and given sampling rate with the consequence that analysts are not often encouraged to think carefully about what an appropriate sampling rate might be. To answer the sampling rate question we propose a novel Bayesian method that incorporates the historical series, cost information and small amounts of pilot data sampled at the faster rate. The heart of our method is a new Bayesian spectral estimation technique that is capable of coherently using data sampled at multiple rates and is demonstrated to have superior practical performance compared with alternatives. Additionally, we introduce a method for hindcasting historical data at the faster rate. A freeware R package, regspec, is available that implements our methods. We illustrate our work by using official statistics time series including the UK consumer price index and counts of UK residents travelling abroad, but our methods are general and apply to any situation where time series data are collected.

KW - Aliasing

KW - Bayesian statistics

KW - Multirate

KW - Spectrum estimation

KW - Time series

UR - http://www.scopus.com/inward/record.url?scp=85006982719&partnerID=8YFLogxK

U2 - 10.1111/rssa.12210

DO - 10.1111/rssa.12210

M3 - Article

VL - 180

SP - 353

EP - 407

JO - Journal of the Royal Statistical Society: Series A (Statistics in Society)

JF - Journal of the Royal Statistical Society: Series A (Statistics in Society)

SN - 0964-1998

IS - 2

ER -