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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/39/1/36 |
| _version_ | 1797580334756265984 |
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| author | Fernanda Catarina Pereira Arminda Manuela Gonçalves Marco Costa |
| author_facet | Fernanda Catarina Pereira Arminda Manuela Gonçalves Marco Costa |
| author_sort | Fernanda Catarina Pereira |
| collection | DOAJ |
| description | 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 outliers in the context of state-space models. In this paper, three methods for detecting and treating outliers are proposed. We also present a comparative study of the proposed methods using data simulated from a local level model with sample sizes of 50 and 500 and with various combinations of parameters, with a 5% contamination error rate of the observation equation. The results were evaluated in terms of the accuracy of model parameters and the forecasts <i>k</i>-steps ahead, as well as the detection rate of true outliers. These methodologies are applied to three real examples. This study shows that the local level model is sufficiently robust even for non-stationary contaminated series, in the sense that they are able to handle non-stationary time series and outliers in a satisfactory way. |
| first_indexed | 2024-03-10T22:48:32Z |
| format | Article |
| id | doaj.art-b081292b0d2541e3916e5dc1b3a3d000 |
| institution | Directory Open Access Journal |
| issn | 2673-4591 |
| language | English |
| last_indexed | 2024-03-10T22:48:32Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj.art-b081292b0d2541e3916e5dc1b3a3d0002023-11-19T10:30:44ZengMDPI AGEngineering Proceedings2673-45912023-06-013913610.3390/engproc2023039036Improving Predictive Accuracy in the Context of Dynamic Modelling of Non-Stationary Time Series with OutliersFernanda Catarina Pereira0Arminda Manuela Gonçalves1Marco Costa2Centre of Mathematics, University of Minho, 4710-057 Braga, PortugalDepartment of Mathematics and Centre of Mathematics, University of Minho, 4710-057 Braga, PortugalCentre for Research and Development in Mathematics and Applications, Águeda School of Technology and Management, University of Aveiro, 3810-193 Aveiro, PortugalMost 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 outliers in the context of state-space models. In this paper, three methods for detecting and treating outliers are proposed. We also present a comparative study of the proposed methods using data simulated from a local level model with sample sizes of 50 and 500 and with various combinations of parameters, with a 5% contamination error rate of the observation equation. The results were evaluated in terms of the accuracy of model parameters and the forecasts <i>k</i>-steps ahead, as well as the detection rate of true outliers. These methodologies are applied to three real examples. This study shows that the local level model is sufficiently robust even for non-stationary contaminated series, in the sense that they are able to handle non-stationary time series and outliers in a satisfactory way.https://www.mdpi.com/2673-4591/39/1/36outlierscontaminated datanon-stationary time seriesstate-space modelsKalman filtersimulation study |
| spellingShingle | Fernanda Catarina Pereira Arminda Manuela Gonçalves Marco Costa Improving Predictive Accuracy in the Context of Dynamic Modelling of Non-Stationary Time Series with Outliers Engineering Proceedings outliers contaminated data non-stationary time series state-space models Kalman filter simulation study |
| title | Improving Predictive Accuracy in the Context of Dynamic Modelling of Non-Stationary Time Series with Outliers |
| title_full | Improving Predictive Accuracy in the Context of Dynamic Modelling of Non-Stationary Time Series with Outliers |
| title_fullStr | Improving Predictive Accuracy in the Context of Dynamic Modelling of Non-Stationary Time Series with Outliers |
| title_full_unstemmed | Improving Predictive Accuracy in the Context of Dynamic Modelling of Non-Stationary Time Series with Outliers |
| title_short | Improving Predictive Accuracy in the Context of Dynamic Modelling of Non-Stationary Time Series with Outliers |
| title_sort | improving predictive accuracy in the context of dynamic modelling of non stationary time series with outliers |
| topic | outliers contaminated data non-stationary time series state-space models Kalman filter simulation study |
| url | https://www.mdpi.com/2673-4591/39/1/36 |
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