Improving Predictive Accuracy in the Context of Dynamic Modelling of Non-Stationary Time Series with Outliers

Improving Predictive Accuracy in the Context of Dynamic Modelling of Non-Stationary Time Series with Outliers

Most real time series exhibit certain characteristics that make the choice of model and its specification difficult. The objective of this study is to address the problem of parameter estimation and the accuracy of forecasts <i>k</i>-steps ahead in non-stationary time series with outlier...

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Bibliographic Details
Main Authors:	Fernanda Catarina Pereira, Arminda Manuela Gonçalves, Marco Costa
Format:	Article
Language:	English
Published:	MDPI AG 2023-06-01
Series:	Engineering Proceedings
Subjects:	outliers contaminated data non-stationary time series state-space models Kalman filter simulation study
Online Access:	https://www.mdpi.com/2673-4591/39/1/36

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