EconPapers    
Economics at your fingertips  
 

Conformal Prediction Bands for Two-Dimensional Functional Time Series

Niccol\`o Ajroldi, Jacopo Diquigiovanni, Matteo Fontana and Simone Vantini

Papers from arXiv.org

Abstract: Time evolving surfaces can be modeled as two-dimensional Functional time series, exploiting the tools of Functional data analysis. Leveraging this approach, a forecasting framework for such complex data is developed. The main focus revolves around Conformal Prediction, a versatile nonparametric paradigm used to quantify uncertainty in prediction problems. Building upon recent variations of Conformal Prediction for Functional time series, a probabilistic forecasting scheme for two-dimensional functional time series is presented, while providing an extension of Functional Autoregressive Processes of order one to this setting. Estimation techniques for the latter process are introduced and their performance are compared in terms of the resulting prediction regions. Finally, the proposed forecasting procedure and the uncertainty quantification technique are applied to a real dataset, collecting daily observations of Sea Level Anomalies of the Black Sea

Date: 2022-07, Revised 2023-07
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Published in Computational Statistics & Data Analysis, 2023, 107821, ISSN 0167-9473

Downloads: (external link)
http://arxiv.org/pdf/2207.13656 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2207.13656

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().

 
Page updated 2023-07-19
Handle: RePEc:arx:papers:2207.13656