On the update frequency of univariate forecasting models

Evangelos Spiliotis and Fotios Petropoulos

European Journal of Operational Research, 2024, vol. 314, issue 1, 111-121

Abstract: In univariate time series forecasting, models are typically updated at every single review period. This practice, which includes specifying the optimal form of the model and estimating its parameters, theoretically allows the models to exploit new information and to respond quickly to possible structural breaks. We argue that such updates may be irrelevant in practice, also unnecessarily increasing computational cost and forecast instability. Using two large data sets of monthly and daily series as well as an indicative family of conventional time series models, we investigate several model updating scenarios, ranging from complete model form specification and parameter estimation at every review period to no updating at all. We find that intermediate updating scenarios, including the re-estimation of specific parameters but not necessarily the specification of the model form, can result in similar or even better accuracy with significantly lower computational cost. We also show that similar conclusions hold true for popular machine learning methods, as well as for setups where different approaches are utilized for training the models or accelerating their specification and estimation. We discuss the implications of our findings for manufacturers, suppliers, and retailers and propose avenues for future advances in the area of model frequency updating.

Keywords: Time series; Model parameters; Model form; Exponential smoothing; Gradient boosting; M competitions (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221723006859
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:314:y:2024:i:1:p:111-121

DOI: 10.1016/j.ejor.2023.08.056

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().