Optimal combination forecasts for hierarchical time series

In many applications, there are multiple time series that are hierarchically organized and can be aggregated at several different levels in groups based on products, geography or some other features. We call these “hierarchical time series”. They are commonly forecast using either a “bottom-up” or a...

Full description

Saved in:

Bibliographic Details
Published in:	Computational statistics & data analysis Vol. 55; no. 9; pp. 2579 - 2589
Main Authors:	Hyndman, Rob J., Ahmed, Roman A., Athanasopoulos, George, Shang, Han Lin
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.09.2011 Elsevier
Series:	Computational Statistics & Data Analysis
Subjects:	Bottom-up forecasting Bottom-up forecasting Combining forecasts GLS regression Hierarchical forecasting Reconciling forecasts Top-down forecasting Combining forecasts Exact sciences and technology Forecasting General topics Geography GLS regression Hierarchical forecasting Hierarchies Inference from stochastic processes; time series analysis Mathematical models Mathematics Multivariate analysis Numerical analysis Numerical analysis. Scientific computation Numerical methods in probability and statistics Optimization Probability and statistics Reconciling forecasts Regression Sciences and techniques of general use Statistics Time series Top-down forecasting Bottom-up forecasting Hierarchical forecasting Top-down forecasting GLS regression Reconciling forecasts Combining forecasts Data analysis Covariance analysis Demand forecasting Time series Prediction theory Statistical estimation Stochastic process Statistical simulation Variance analysis Variance Statistical regression Forecasting theory Unbiased estimation Statistical computation Regression model Filtering theory Autocorrelation Method study
ISSN:	0167-9473, 1872-7352
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!