Machele, ILOnumanyi, Adeiza JAbu-Mahfouz, Adnan MIKurien, A2026-03-102026-03-102025-122169-3536DOI: 10.1109/ACCESS.2025.3647525http://hdl.handle.net/10204/14731In this paper, we introduce a multidimensional discriminant analysis-based method (MDAM), which is a modality testing method designed to determine whether an unknown input multidimensional time series is unimodal or multimodal. Existing unimodality testing methods face several key limitations: 1) they are primarily designed for unidimensional data and struggle with multidimensional extensions, 2) they rely on probability density function (PDF)-based approaches that fail in the presence of overlapping distributions, skewed data, and noise, and 3) they often misinterpret multimodal structures due to misleading PDF-based marginal analysis. To address these challenges, MDAM leverages a novel function that integrates the between-class mean and variance variables using a discriminant analysis approach. This distribution-independent method effectively detects modality variations across both mean and variance parameters, making it well-suited for high-dimensional and complex datasets. Comparative analysis based on synthetic and real datasets revealed that MDAM consistently outperformed five state-of-the-art techniques such as Folding, Runt, KS, DAT, and Dip, across unidimensional, multidimensional, balanced, unbalanced, unimodal, and multimodal datasets. Notably, MDAM achieved a high average accuracy of 99.8% across all dataset types, with a 20% to 40% accuracy improvement over the next-best algorithms in multimodal and mixed distributions. Its robustness across various evaluation metrics, including precision, recall, and F1 score, further establishes MDAM as a reliable tool for modality testing in time series datasets.FulltextenMultidimensional discriminant analysis-based methodMDAMProbability density functionPDFArticleN/A