Forecasting Time Series with Multiple Seasonal Patterns
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Altmetric Citations
Gould, Phillip G; Koehler, Anne B.; Ord, J Keith; Snyder, Ralph; Hyndman, Rob; Vahid, Farshid
Description
A new approach is proposed for forecasting a time series with multiple seasonal patterns. A state space model is developed for the series using the innovations approach which enables us to develop explicit models for both additive and multiplicative seasonality. Parameter estimates may be obtained using methods from exponential smoothing. The proposed model is used to examine hourly and daily patterns in hourly data for both utility loads and traffic flows. Our formulation provides a model for...[Show more]
dc.contributor.author | Gould, Phillip G | |
---|---|---|
dc.contributor.author | Koehler, Anne B. | |
dc.contributor.author | Ord, J Keith | |
dc.contributor.author | Snyder, Ralph | |
dc.contributor.author | Hyndman, Rob | |
dc.contributor.author | Vahid, Farshid | |
dc.date.accessioned | 2015-12-07T22:21:08Z | |
dc.identifier.issn | 0377-2217 | |
dc.identifier.uri | http://hdl.handle.net/1885/19898 | |
dc.description.abstract | A new approach is proposed for forecasting a time series with multiple seasonal patterns. A state space model is developed for the series using the innovations approach which enables us to develop explicit models for both additive and multiplicative seasonality. Parameter estimates may be obtained using methods from exponential smoothing. The proposed model is used to examine hourly and daily patterns in hourly data for both utility loads and traffic flows. Our formulation provides a model for several existing seasonal methods and also provides new options, which result in superior forecasting performance over a range of prediction horizons. In particular, seasonal components can be updated more frequently than once during a seasonal cycle. The approach is likely to be useful in a wide range of applications involving both high and low frequency data, and it handles missing values in a straightforward manner. | |
dc.publisher | Elsevier | |
dc.source | European Journal of Operational Research | |
dc.subject | Keywords: Data transfer; Mathematical models; Operations research; Parameter estimation; State space methods; Utility programs; Exponential smoothing; Multiple seasonal patterns; Seasonality; Time series analysis Exponential smoothing; Forecasting; Seasonality; State space models; Time series | |
dc.title | Forecasting Time Series with Multiple Seasonal Patterns | |
dc.type | Journal article | |
local.description.notes | Imported from ARIES | |
local.identifier.citationvolume | 191 | |
dc.date.issued | 2008 | |
local.identifier.absfor | 140303 - Economic Models and Forecasting | |
local.identifier.ariespublication | u4137903xPUB10 | |
local.type.status | Published Version | |
local.contributor.affiliation | Gould, Phillip G, Monash University | |
local.contributor.affiliation | Koehler, Anne B., University of Miami | |
local.contributor.affiliation | Ord, J Keith, University of Gerogetown | |
local.contributor.affiliation | Snyder, Ralph, Monash University | |
local.contributor.affiliation | Hyndman, Rob, Monash University | |
local.contributor.affiliation | Vahid, Farshid, College of Business and Economics, ANU | |
local.description.embargo | 2037-12-31 | |
local.bibliographicCitation.startpage | 205 | |
local.bibliographicCitation.lastpage | 220 | |
local.identifier.doi | 10.1016/j.ejor.2007.08.024 | |
dc.date.updated | 2015-12-07T08:54:16Z | |
local.identifier.scopusID | 2-s2.0-43049096096 | |
local.identifier.thomsonID | 000257186700016 | |
Collections | ANU Research Publications |
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