A study of outliers in the exponential smoothing approach to forecasting

Anne B Koehler, Ralph David Snyder, J Keith Ord, Adrian Nicholas Beaumont

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Outliers in time series have the potential to affect parameter estimates and forecasts when using exponential smoothing. The aim of this study is to show the way in which important types of outliers can be incorporated into linear innovations state space models for exponential smoothing methods. The types of outliers include an additive outlier, a level shift, and a transitory change. The general innovations state space model and a special case which encompasses the common linear exponential smoothing methods are examined. A method for identifying outliers using innovations state space models is proposed. This method is investigated using both simulations and applications to real time series. The impact of an outlier s location on the forecasts and the estimation of parameters is examined. The forecasts from outlier and basic non-outlier models are compared. An automatic method is found to result in improved forecasts for both the simulated and real data.
Original languageEnglish
Pages (from-to)477 - 484
Number of pages8
JournalInternational Journal of Forecasting
Volume28
Issue number2
DOIs
Publication statusPublished - 2012

Cite this

Koehler, Anne B ; Snyder, Ralph David ; Ord, J Keith ; Beaumont, Adrian Nicholas. / A study of outliers in the exponential smoothing approach to forecasting. In: International Journal of Forecasting. 2012 ; Vol. 28, No. 2. pp. 477 - 484.
@article{3f6004e315ed4b6fad35aa37d656b451,
title = "A study of outliers in the exponential smoothing approach to forecasting",
abstract = "Outliers in time series have the potential to affect parameter estimates and forecasts when using exponential smoothing. The aim of this study is to show the way in which important types of outliers can be incorporated into linear innovations state space models for exponential smoothing methods. The types of outliers include an additive outlier, a level shift, and a transitory change. The general innovations state space model and a special case which encompasses the common linear exponential smoothing methods are examined. A method for identifying outliers using innovations state space models is proposed. This method is investigated using both simulations and applications to real time series. The impact of an outlier s location on the forecasts and the estimation of parameters is examined. The forecasts from outlier and basic non-outlier models are compared. An automatic method is found to result in improved forecasts for both the simulated and real data.",
author = "Koehler, {Anne B} and Snyder, {Ralph David} and Ord, {J Keith} and Beaumont, {Adrian Nicholas}",
year = "2012",
doi = "10.1016/j.ijforecast.2011.05.001",
language = "English",
volume = "28",
pages = "477 -- 484",
journal = "International Journal of Forecasting",
issn = "0169-2070",
publisher = "Elsevier",
number = "2",

}

A study of outliers in the exponential smoothing approach to forecasting. / Koehler, Anne B; Snyder, Ralph David; Ord, J Keith; Beaumont, Adrian Nicholas.

In: International Journal of Forecasting, Vol. 28, No. 2, 2012, p. 477 - 484.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - A study of outliers in the exponential smoothing approach to forecasting

AU - Koehler, Anne B

AU - Snyder, Ralph David

AU - Ord, J Keith

AU - Beaumont, Adrian Nicholas

PY - 2012

Y1 - 2012

N2 - Outliers in time series have the potential to affect parameter estimates and forecasts when using exponential smoothing. The aim of this study is to show the way in which important types of outliers can be incorporated into linear innovations state space models for exponential smoothing methods. The types of outliers include an additive outlier, a level shift, and a transitory change. The general innovations state space model and a special case which encompasses the common linear exponential smoothing methods are examined. A method for identifying outliers using innovations state space models is proposed. This method is investigated using both simulations and applications to real time series. The impact of an outlier s location on the forecasts and the estimation of parameters is examined. The forecasts from outlier and basic non-outlier models are compared. An automatic method is found to result in improved forecasts for both the simulated and real data.

AB - Outliers in time series have the potential to affect parameter estimates and forecasts when using exponential smoothing. The aim of this study is to show the way in which important types of outliers can be incorporated into linear innovations state space models for exponential smoothing methods. The types of outliers include an additive outlier, a level shift, and a transitory change. The general innovations state space model and a special case which encompasses the common linear exponential smoothing methods are examined. A method for identifying outliers using innovations state space models is proposed. This method is investigated using both simulations and applications to real time series. The impact of an outlier s location on the forecasts and the estimation of parameters is examined. The forecasts from outlier and basic non-outlier models are compared. An automatic method is found to result in improved forecasts for both the simulated and real data.

U2 - 10.1016/j.ijforecast.2011.05.001

DO - 10.1016/j.ijforecast.2011.05.001

M3 - Article

VL - 28

SP - 477

EP - 484

JO - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

IS - 2

ER -