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Disaggregating Time-Series with Many Indicators: An Overview of the DisaggregateTS Package

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Disaggregating Time-Series with Many Indicators: An Overview of the DisaggregateTS Package. / Mosley, Luke; Salehzadeh Nobari, Kaveh; Brandi , Giuseppe et al.
In: The R Journal, Vol. 16, No. 4, 27.06.2025, p. 62-73.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Mosley, L, Salehzadeh Nobari, K, Brandi , G & Gibberd, A 2025, 'Disaggregating Time-Series with Many Indicators: An Overview of the DisaggregateTS Package', The R Journal, vol. 16, no. 4, pp. 62-73. https://doi.org/10.32614/RJ-2024-035

APA

Mosley, L., Salehzadeh Nobari, K., Brandi , G., & Gibberd, A. (2025). Disaggregating Time-Series with Many Indicators: An Overview of the DisaggregateTS Package. The R Journal, 16(4), 62-73. https://doi.org/10.32614/RJ-2024-035

Vancouver

Mosley L, Salehzadeh Nobari K, Brandi G, Gibberd A. Disaggregating Time-Series with Many Indicators: An Overview of the DisaggregateTS Package. The R Journal. 2025 Jun 27;16(4):62-73. doi: 10.32614/RJ-2024-035

Author

Mosley, Luke ; Salehzadeh Nobari, Kaveh ; Brandi , Giuseppe et al. / Disaggregating Time-Series with Many Indicators : An Overview of the DisaggregateTS Package. In: The R Journal. 2025 ; Vol. 16, No. 4. pp. 62-73.

Bibtex

@article{dd84f6c27cf94135ba8002600d9cc193,
title = "Disaggregating Time-Series with Many Indicators: An Overview of the DisaggregateTS Package",
abstract = "Low-frequency time-series (e.g., quarterly data) are often treated as benchmarks for interpolating to higher frequencies, since they generally exhibit greater precision and accuracy in contrast to their high-frequency counterparts (e.g., monthly data) reported by governmental bodies. An array of regression-based methods have been proposed in the literature which aim to estimate a target high-frequency series using higher frequency indicators. However, in the era of big data and with the prevalence of large volumes of administrative data-sources there is a need to extend traditional methods to work in high-dimensional settings, i.e., where the number of indicators is similar or larger than the number of low-frequency samples. The package DisaggregateTS includes both classical regressions-based disaggregation methods alongside recent extensions to high-dimensional settings. This paper provides guidance on how to implement these methods via the package in R, and demonstrates their use in an application to disaggregating CO2 emissions.",
author = "Luke Mosley and {Salehzadeh Nobari}, Kaveh and Giuseppe Brandi and Alex Gibberd",
year = "2025",
month = jun,
day = "27",
doi = "10.32614/RJ-2024-035",
language = "English",
volume = "16",
pages = "62--73",
journal = "The R Journal",
issn = "2073-4859",
publisher = "R Foundation for Statistical Computing",
number = "4",

}

RIS

TY - JOUR

T1 - Disaggregating Time-Series with Many Indicators

T2 - An Overview of the DisaggregateTS Package

AU - Mosley, Luke

AU - Salehzadeh Nobari, Kaveh

AU - Brandi , Giuseppe

AU - Gibberd, Alex

PY - 2025/6/27

Y1 - 2025/6/27

N2 - Low-frequency time-series (e.g., quarterly data) are often treated as benchmarks for interpolating to higher frequencies, since they generally exhibit greater precision and accuracy in contrast to their high-frequency counterparts (e.g., monthly data) reported by governmental bodies. An array of regression-based methods have been proposed in the literature which aim to estimate a target high-frequency series using higher frequency indicators. However, in the era of big data and with the prevalence of large volumes of administrative data-sources there is a need to extend traditional methods to work in high-dimensional settings, i.e., where the number of indicators is similar or larger than the number of low-frequency samples. The package DisaggregateTS includes both classical regressions-based disaggregation methods alongside recent extensions to high-dimensional settings. This paper provides guidance on how to implement these methods via the package in R, and demonstrates their use in an application to disaggregating CO2 emissions.

AB - Low-frequency time-series (e.g., quarterly data) are often treated as benchmarks for interpolating to higher frequencies, since they generally exhibit greater precision and accuracy in contrast to their high-frequency counterparts (e.g., monthly data) reported by governmental bodies. An array of regression-based methods have been proposed in the literature which aim to estimate a target high-frequency series using higher frequency indicators. However, in the era of big data and with the prevalence of large volumes of administrative data-sources there is a need to extend traditional methods to work in high-dimensional settings, i.e., where the number of indicators is similar or larger than the number of low-frequency samples. The package DisaggregateTS includes both classical regressions-based disaggregation methods alongside recent extensions to high-dimensional settings. This paper provides guidance on how to implement these methods via the package in R, and demonstrates their use in an application to disaggregating CO2 emissions.

U2 - 10.32614/RJ-2024-035

DO - 10.32614/RJ-2024-035

M3 - Journal article

VL - 16

SP - 62

EP - 73

JO - The R Journal

JF - The R Journal

SN - 2073-4859

IS - 4

ER -