Forecasting Time-Series with Correlated Seasonality
Anne B. Koehler,
Farshid Vahid (),
Ralph Snyder (),
Keith Ord () and
Rob Hyndman ()
No 28/04, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics
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 single source of error approach which enables us to develop explicit models for both additive and multiplicative seasonality. Parameter estimates may be obtained using methods adapted from general exponential smoothing, although the Kalman filter may also be used. 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. 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.
Keywords: Exponential smoothing; Holt-Winters; Seasonality; Structural time series model (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2004-12, Revised 2005-10
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2004/wp28-04.pdf Revised version, October 2005 (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:msh:ebswps:2004-28
Ordering information: This working paper can be ordered from
http://business.mona ... -business-statistics
Access Statistics for this paper
More papers in Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics PO Box 11E, Monash University, Victoria 3800, Australia. Contact information at EDIRC.
Bibliographic data for series maintained by Dr Xibin Zhang ().