Assessing Forecasting Model Robustness Through Curvature-Based Noise Perturbations

Lynda Ayachi

2024

Abstract

This paper introduces a novel approach to robustness testing of forecasting models through the use of curvature-based noise perturbations. Traditional noise models, such as Gaussian and uniform noise, often fail to capture the complex structural variations inherent in real-world time series data. By calculating the curvature of a time series and selectively perturbing curvature values, we generate a new type of noise that directly alters the shape and smoothness of the data. This method provides a unique perspective on model performance, revealing sensitivities to structural changes that conventional noise types do not address. Our analysis demonstrates the impact of curvature distortions on seasonality, trend, and overall model accuracy, highlighting vulnerabilities in forecasting models that are otherwise masked by standard robustness tests. Results show that curvature-based noise significantly affects the ability of models to accurately predict future values, especially in the presence of cyclical and seasonal patterns. The findings suggest that incorporating curvature perturbations into robustness evaluations can provide deeper insights into model resilience and guide the development of more adaptable forecasting techniques.

Paper Citation

in Harvard Style

Ayachi L. (2024). Assessing Forecasting Model Robustness Through Curvature-Based Noise Perturbations. In Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA; ISBN 978-989-758-721-4, SciTePress, pages 488-495. DOI: 10.5220/0013061600003837

in Bibtex Style

@conference{ncta24,
author={Lynda Ayachi},
title={Assessing Forecasting Model Robustness Through Curvature-Based Noise Perturbations},
booktitle={Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA},
year={2024},
pages={488-495},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013061600003837},
isbn={978-989-758-721-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA
TI - Assessing Forecasting Model Robustness Through Curvature-Based Noise Perturbations
SN - 978-989-758-721-4
AU - Ayachi L.
PY - 2024
SP - 488
EP - 495
DO - 10.5220/0013061600003837
PB - SciTePress