Improving Predictive Accuracy in the Context of Dynamic Modelling of Non-Stationary Time Series with Outliers-Reference-Cited by-同舟云学术

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

Published:2023-06-29 Issue: Volume: Page:
ISSN:
Container-title:ITISE 2023
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Author:

Pereira Fernanda Catarina¹^ORCID,Gonçalves Arminda Manuela²^ORCID,Costa Marco³^ORCID

Affiliation:

1. Centre of Mathematics, University of Minho, 4710-057 Braga, Portugal

2. Department of Mathematics and Centre of Mathematics, University of Minho, 4710-057 Braga, Portugal

3. Centre for Research and Development in Mathematics and Applications, Águeda School of Technology and Management, University of Aveiro, 3810-193 Aveiro, Portugal

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/39/1/36/pdf

Reference16 articles.

1. Triantafyllopoulos, K. (2021). Bayesian Inference of State Space Models, Springer Texts in Statistics; Springer.

2. A guide to state–space modeling of ecological time series;Newman;Ecol. Monogr.,2021

3. Wu, H., Matteson, D., and Wells, M. (2022). Interpretable Latent Variables in Deep State Space Models. arXiv.

4. Matsuura, K. (2022). Bayesian Statistical Modeling with Stan, R, and Python, Springer.

5. Monteiro, M., and Costa, M. (2023). Change Point Detection by State Space Modeling of Long-Term Air Temperature Series in Europe. Stats, 6.

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