Disaggregating time-series with many indicators: an overview of the disaggregateTS package

Mosley, Luke, Salehzadeh Nobari, Kaveh ORCID: 0000-0002-4053-0781, Brandi, Giuseppe and Gibberd, Alex (2025) Disaggregating time-series with many indicators: an overview of the disaggregateTS package. The R Journal, 16 (4). pp. 62-73. ISSN 2073-4859

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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.

Item Type:	Article
Additional Information:	© 2025 The Author(s)
Divisions:	Psychological and Behavioural Science
Date Deposited:	31 Jul 2025 12:03
Last Modified:	04 Nov 2025 18:22
URI:	http://eprints.lse.ac.uk/id/eprint/128975

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