Exponential smoothing with regressors: Estimation and initialization

Ahmad Farid Osman, Maxwell Leslie King

Research output: Contribution to journalArticleResearchpeer-review

Abstract

The main objective of this paper is to outline the estimation and initialization procedures for the exponential smoothing with regressors forecasting approach which was recently introduced. The paper also discusses what restrictions need to be imposed during the estimation process so that the algorithm satisfies the forecastability conditions. An empirical study using real non-seasonal data shows that the new approach sometimes has the ability to produce better forecasts than the existing exponential smoothing methods without regressors.
Original languageEnglish
Pages (from-to)253 - 263
Number of pages11
JournalModel Assisted Statistics and Applications
Volume10
Issue number3
DOIs
Publication statusPublished - 2015

Cite this

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title = "Exponential smoothing with regressors: Estimation and initialization",
abstract = "The main objective of this paper is to outline the estimation and initialization procedures for the exponential smoothing with regressors forecasting approach which was recently introduced. The paper also discusses what restrictions need to be imposed during the estimation process so that the algorithm satisfies the forecastability conditions. An empirical study using real non-seasonal data shows that the new approach sometimes has the ability to produce better forecasts than the existing exponential smoothing methods without regressors.",
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Exponential smoothing with regressors: Estimation and initialization. / Osman, Ahmad Farid; King, Maxwell Leslie.

In: Model Assisted Statistics and Applications, Vol. 10, No. 3, 2015, p. 253 - 263.

Research output: Contribution to journalArticleResearchpeer-review

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AB - The main objective of this paper is to outline the estimation and initialization procedures for the exponential smoothing with regressors forecasting approach which was recently introduced. The paper also discusses what restrictions need to be imposed during the estimation process so that the algorithm satisfies the forecastability conditions. An empirical study using real non-seasonal data shows that the new approach sometimes has the ability to produce better forecasts than the existing exponential smoothing methods without regressors.

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DO - 10.3233/MAS-150329

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JO - Model Assisted Statistics and Applications

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