A set-Membership Identification Algorithm for Time-Series Modeling Using ARMA Process

This paper focuses on the identification of ARMA models for time-series analysis. The proposed approach is developed in the Unknown But Bounded Error framework. Two Ellipsoidal Outer Bounding (EOB) identification algorithms are proposed. The second algorithm is an extended version of the first one. The paper includes stability and convergence analysis. Some numerical simulations are conducted to validate the analysis and assess the effectiveness of the proposed algorithms.

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Publication Details

Published
2024-11-03
DOI
https://doi.org/10.1109/icsc63929.2024.10928846
Citations
1
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
0.35
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article

A set-Membership Identification Algorithm for Time-Series Modeling Using ARMA Process

Miloud Frikel, Saïd Safi, Mathieu Pouliquen, Hasna El Maizi
1 citations
Neural Networks and Applications
0.35
article

A set-Membership Identification Algorithm for Time-Series Modeling Using ARMA Process

Miloud Frikel, Saïd Safi, Mathieu Pouliquen, Hasna El Maizi
article en
1 citations

Abstract

This paper focuses on the identification of ARMA models for time-series analysis. The proposed approach is developed in the Unknown But Bounded Error framework. Two Ellipsoidal Outer Bounding (EOB) identification algorithms are proposed. The second algorithm is an extended version of the first one. The paper includes stability and convergence analysis. Some numerical simulations are conducted to validate the analysis and assess the effectiveness of the proposed algorithms.

École Nationale Supérieure d'Ingénieurs de Caen (FR), Normandie Université (FR), Laboratoire d’Informatique et Systèmes (FR), Université Sultan Moulay Slimane (MA), Université de Caen Normandie (FR)
Openalex Percentile: Top 29%
Neural Networks and Applications
0.35
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