Speaker
Description
Functional data analysis has established itself as a powerful framework for analyzing data recorded over continuous domains such as time. Within this context, functional motif discovery refers to the identification of recurrent patterns that appear multiple times across different portions of a single curve and/or within misaligned portions of multiple curves. In this study, we explore the integration of functional motif discovery into statistical learning pipelines to enhance the predictive performance of data-driven models. By identifying recurring and informative temporal patterns within functional data, motif discovery enables the extraction of meaningful features that can improve model accuracy and interpretability. We propose a novel framework that combines functional motif extraction with machine learning algorithms to strengthen forecasting capabilities in predictive tasks. Specifically, we employ two advanced statistical techniques, probKMA (Cremona and Chiaromonte, 2023) and funBIalign (Di Iorio et al., 2025), to uncover recurring motifs in functional data, which are subsequently incorporated as input features in prediction models. The approach is evaluated using continuous glucose monitoring data from individuals with type 1 diabetes in real-world physical activity settings, as collected in the Type 1 Diabetes Exercise Initiative (T1DEXI) study (Jaeb Center for Health Research, 2020). The high variability and complexity of these real-world data can pose substantial challenges for prediction (Neumann et al., 2025), but they also reveal significant potential for improvement through functional motif discovery. Overall, this research investigates how the proposed framework can uncover latent structure in glucose dynamics and support more accurate predictive modeling. More broadly, it highlights the value of integrating functional data analysis, and particularly functional motif discovery, into machine learning workflows to enhance interpretability, robustness, and performance across domains involving complex temporal data.
References:
Cremona, M. A. and Chiaromonte, F. (2023). Probabilistic k -means with local alignment for clustering and motif discovery in functional data. Journal of Computational and Graphical Statistics, 32(3):1119-1130.
Di Iorio, J., Cremona, M. A., and Chiaromonte, F. (2025). funbialign: a hierachical algorithm for functional motif discovery based on mean squared residue scores. Statistics and computing, 35(1):11.
Jaeb Center for Health Research (2020). Type 1 diabetes exercise initiative: The effect of exercise on glycemic control in type 1 diabetes study.
Neumann, A., Zghal, Y., Cremona, M. A., Hajji, A., Morin, M., and Rekik, M. (2025). A data-driven personalized approach to predict blood glucose levels in type-1 diabetes patients exercising in free-living conditions. Computers in biology and medicine, 190:110015
21429405355