Simultaneous model construction and noise reduction for hierarchical time series via Support Vector Regression

Simple item page
dc.coverageDOI: 10.1016/j.knosys.2021.107492
dc.creatorKarmy, Juan Pablo
dc.creatorLópez, Julio
dc.creatorMaldonado, Sebastián
dc.date2021
dc.date.accessioned2026-01-05T21:16:56Z
dc.date.available2026-01-05T21:16:56Z
dc.description<p>In several applications, there are hierarchically-organized time series that can be aggregated at various levels. In this paper, a novel Support Vector Regression approach is proposed for dealing with hierarchical time series forecasting. The main idea is to pool information across levels of hierarchy, preventing bottom-level series from deviate much with respect to the series at the upper levels. The reasoning behind this approach is to estimate robust bottom-level models that can deal with the intrinsic noise present at this level due to the lack of information. Two variants are presented: First, we solve a single optimization problem that constructs all the related regression functions together, relating the bottom level series with the root node, while the second variant pools relates the leaf nodes with their respective parent nodes. The proposed approach showed best performance when compared with the state of the art on hierarchical time series forecasting using well-known benchmark datasets.</p>eng
dc.identifierhttps://investigadores.uandes.cl/en/publications/c896a7cb-975f-4b7f-9386-a08c0bc524f2
dc.identifier.urihttps://repositorio.uandes.cl/handle/uandes/67311
dc.languageeng
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourcevol.232 (2021) date: 2021-11-28
dc.subjectHeterogeneity control
dc.subjectHierarchical time series
dc.subjectSupport vector machines
dc.subjectSupport Vector Regression
dc.subjectTime series forecasting
dc.titleSimultaneous model construction and noise reduction for hierarchical time series via Support Vector Regressioneng
dc.typeArticleeng
dc.typeArtículospa
Files
Collections
Pure-CRIS