Systems and Means of Informatics
2014, Volume 24, Issue 2, pp 23-36
RECONCILIATION OF AGGREGATED AND DISAGGREGATED
TIME SERIES FORECASTS IN NONPARAMETRIC FORECASTING
PROBLEMS
- M.M. Stenina
- V. V. Strijov
Abstract
In many applications, there are the problems of forecasting a lot of time
series with hierarchical structure. It is needed to reconcile forecasts across the hierarchy. In this paper, a new algorithm of reconciliation of hierarchical time series
forecasts is proposed. This algorithmis based on solving the optimization problem
with constraints. The proposed algorithm allows to reconcile the forecasts with
nonplanar hierarchical structure and to take into account physical constraints
of forecasted values such as nonnegativeness or maximal value. The algorithm
performance is illustrated by the railroad stations occupancy data in the Omsk
region. The quality of forecasts is compared with the quality of forecasts made
by the optimal algorithm of reconciliation. Also, the algorithm performance is
demonstrated for the nonplanar hierarchical structure of time series.
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[+] About this article
Title
RECONCILIATION OF AGGREGATED AND DISAGGREGATED
TIME SERIES FORECASTS IN NONPARAMETRIC FORECASTING
PROBLEMS
Journal
Systems and Means of Informatics
Volume 24, Issue 2, pp 23-36
Cover Date
2013-11-30
DOI
10.14357/08696527140202
Print ISSN
0869-6527
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
hierarchical time series; nonparametric forecasting; empirical distri-
bution; forecasts reconciliation
Authors
M.M. Stenina  and V. V. Strijov 
Author Affiliations
Moscow Institute of Physics and Technology, 9 Institutskiy Per., Dolgoprudny, Moscow Region 141700, Russian Federation
Dorodnicyn Computing Center, Russian Academy of Sciences, 40 Vavilov Str.,
Moscow 119333, Russian Federation
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