Consuelo Bridda is a PhD student in Modeling and Data Science at the Department of Computer Science, University of Turin, working within the National Research Centre for High Performance Computing, Big Data and Quantum Computing.
Her research focuses on Artificial Intelligence for Science (AI4Science), exploring the application of artificial intelligence and data-driven methods to scientific problems across different disciplines. She holds a Master’s degree in Analytical Chemistry from the University of Turin. Her research activities have included time series analysis and interdisciplinary projects combining artificial intelligence with medicinal chemistry and biology.
Consuelo Bridda, Doriana Medić, Lavinia Chiara Tagliabue, Robert Birke, Marco Aldinucci
Accepted for publication in: 2026 IEEE International Symposium on Precision Clock Synchronization for Measurement, Control, and Communication (ISPCS)
ai
@inproceedings{bridda2026t2f,
author = {Consuelo Bridda and Doriana Medi{\'c} and Lavinia Chiara Tagliabue and Robert Birke and Marco Aldinucci},
title = {T2F: Forecasting Irregularly Sampled Time Series via Time Features},
booktitle = {Accepted for publication in: 2026 IEEE International Symposium on Precision Clock Synchronization for Measurement, Control, and Communication (ISPCS)},
year = {2026},
address = {Brescia, Italy},
keywords = {ai}
}
Consuelo Bridda, Doriana Medić, Lavinia Chiara Tagliabue, Robert Birke, Marco Aldinucci
ai
Time series forecasting under irregular sampling is challenging because temporal information cannot be inferred solely from sample ordering. Although several neural architectures have been specifically developed for irregularly sampled data, the extent to which forecasting performance depends on model complexity rather than temporal representation remains unclear. In this work, we investigate T2F, a feature-based framework for forecasting regular and irregular time series. The forecasting task is reformulated as a supervised learning problem in which temporal information is explicitly encoded through engineered features. In particular, we compare absolute timestamp representations with local time-difference (Δt) encoding, which captures the temporal spacing between observations and between the last observation and the prediction target. T2F is evaluated via an MLP model on synthetic benchmarks and real-world energy consumption data under both regular and irregular sampling conditions. Experimental comparisons include naive baselines, linear models, tree-based methods, and GRU-D, a recurrent architecture designed for irregular time series. Results show that forecasting performance under irregular sampling depends strongly on temporal representation. While absolute timestamps provide useful global temporal context, local Δt encoding consistently improves predictive accuracy, particularly in short-term forecasting. In the considered real-world irregular setting, the MLP with Δt encoding outperforms traditional baselines and GRU-D despite its substantially simpler architecture. Overall, the results suggest that carefully designed temporal representations can make simple feed-forward models highly competitive for moderately irregular short-term forecasting, with no specialized recurrent or continuous-time architectures.
@misc{26:bridda:t2f-ispcs,
title = {{T2F}: Forecasting Irregularly Sampled Time Series via Time Features},
author = {Consuelo Bridda and Doriana Medi{\'c} and Lavinia Chiara Tagliabue and Robert Birke and Marco Aldinucci},
year = 2026,
month = oct,
address = {Brescia, Italy},
url = {https://datacloud.di.unito.it/index.php/s/rnNkS6ZkCTNPoTL},
note = {Presented by Consuelo Bridda on 7 October 2026},
keywords = {ai},
howpublished = {2026 IEEE International Symposium on Precision Clock Synchronization for Measurement, Control, and Communication (ISPCS)},
abstract = {Time series forecasting under irregular sampling is challenging because temporal information cannot be inferred solely from sample ordering. Although several neural architectures have been specifically developed for irregularly sampled data, the extent to which forecasting performance depends on model complexity rather than temporal representation remains unclear. In this work, we investigate T2F, a feature-based framework for forecasting regular and irregular time series. The forecasting task is reformulated as a supervised learning problem in which temporal information is explicitly encoded through engineered features. In particular, we compare absolute timestamp representations with local time-difference (Δt) encoding, which captures the temporal spacing between observations and between the last observation and the prediction target. T2F is evaluated via an MLP model on synthetic benchmarks and real-world energy consumption data under both regular and irregular sampling conditions. Experimental comparisons include naive baselines, linear models, tree-based methods, and GRU-D, a recurrent architecture designed for irregular time series. Results show that forecasting performance under irregular sampling depends strongly on temporal representation. While absolute timestamps provide useful global temporal context, local Δt encoding consistently improves predictive accuracy, particularly in short-term forecasting. In the considered real-world irregular setting, the MLP with Δt encoding outperforms traditional baselines and GRU-D despite its substantially simpler architecture. Overall, the results suggest that carefully designed temporal representations can make simple feed-forward models highly competitive for moderately irregular short-term forecasting, with no specialized recurrent or continuous-time architectures.}
}
