More accurate prediction intervals for exponential smoothing with covariates with applications in electrical load forecasting and sales forecasting (Articolo in rivista)

Type

• Prodotto della ricerca (Classe)
• Articolo in rivista (Classe)

Label

• More accurate prediction intervals for exponential smoothing with covariates with applications in electrical load forecasting and sales forecasting (Articolo in rivista) (literal)

Anno

• 2014-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

• 10.1002/qre.1625 (literal)

Alternative label

• Rainer Göb; Kristina Lurz; Antonio Pievatolo (2014)
  More accurate prediction intervals for exponential smoothing with covariates with applications in electrical load forecasting and sales forecasting
  in Quality and reliability engineering international (Online); John Wiley & Sons Ltd., Chichester (Regno Unito)
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Rainer Göb; Kristina Lurz; Antonio Pievatolo (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

• http://onlinelibrary.wiley.com/doi/10.1002/qre.1625/abstract (literal)

Rivista

• Quality and reliability engineering international (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• Institute for Applied Mathematics and Statistics, University of Würzburg, Sanderring 2, D-97070 Würzburg, Germany; CNR-IMATI, Via Bassini 15, I-20133 Milano, Italy (literal)

Titolo

• More accurate prediction intervals for exponential smoothing with covariates with applications in electrical load forecasting and sales forecasting (literal)

Abstract

• The most widely used prediction intervals in empirical time series analysis are of plug-in type; that is, the empirical estimates of model parameters are inserted into formulae for prediction intervals that are obtained from a theoretical analysis of the time series model. Several authors have pointed out that such model-based prediction intervals are too narrow, that is, that the actual confidence level is smaller than the nominal confidence level. The reason is that the uncertainty contained in the parameter estimates is not taken into account in the prediction interval. We investigate this problem for exponential smoothing under covariates with additive trend and additive season. We determine alternative prediction intervals by analysing a linearisation of the underlying model with linear model methods. Two simulation studies based on electricity load data and on sales data confirm the reservations about the plug-in prediction intervals, whereas the intervals based on the linearisation approach are significantly better, and perform very well, with actual confidence levels close to the nominal. (literal)

Editore

• John Wiley & Sons Ltd. (Editore)

Prodotto di

• Sviluppo ed applicazione di modelli statistici (SP.P01.004.001) (Modulo)
• Istituto di matematica applicata e tecnologie informatiche \"Enrico Magenes\" (IMATI) (Istituto)

Autore CNR

• ANTONIO PIEVATOLO (Unità di personale interno)

Insieme di parole chiave

• Parole chiave di "More accurate prediction intervals for exponential smoothing with covariates with applications in electrical load forecasting and sales forecasting" (Insieme di parole chiave)

Incoming links:

Prodotto

• Istituto di matematica applicata e tecnologie informatiche \"Enrico Magenes\" (IMATI) (Istituto)
• Sviluppo ed applicazione di modelli statistici (SP.P01.004.001) (Modulo)

Autore CNR di

• ANTONIO PIEVATOLO (Unità di personale interno)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• Quality and reliability engineering international (Rivista)

Editore di

• John Wiley & Sons Ltd. (Editore)

Insieme di parole chiave di

• Parole chiave di "More accurate prediction intervals for exponential smoothing with covariates with applications in electrical load forecasting and sales forecasting" (Insieme di parole chiave)