ENBIS-26 Conference

Session

Time Series, Forecasting and Dynamic Systems

Sep 9, 2026, 10:55 AM

Conveners

Time Series, Forecasting and Dynamic Systems

• Ouassim Feliachi (RTE)

43. A Semi-Supervised Framework for Optimisation-Based Label Inference in RUL Prediction

Joseph Cupit (The University of Manchester)

9/9/26, 10:55 AM

Time Series, Forecasting and Dynamic Systems

Industrial systems generate large volumes of operational data, enabling predictive maintenance strategies to reduce unplanned downtime and costs. Over the past decade, machine learning (ML) models have been widely used for predicting equipment degradation. However, their effectiveness is constrained by the scarcity of high-quality labels, as industrial datasets remain largely unlabelled....

51. Diffusion Models for Multivariate Probabilistic Net Load Forecasting in Transmission Grid Congestion Management

Mr Maxime Ducourau (EPFL)

9/9/26, 11:15 AM

Time Series, Forecasting and Dynamic Systems

Short-term congestion risk assessment in transmission grids is still largely based on deterministic load flow computations from point forecasts. We investigate multivariate probabilistic net load forecasting across substations as a way to better quantify the probability of congestion events, which arise from correlated forecast errors across substations. This is particularly challenging with...

79. INAR(1) Processes based on the Zipf-PSS distribution

Marta Perez-Casany (Universitat Politècnica de Catalunya)

9/9/26, 11:35 AM

Time Series, Forecasting and Dynamic Systems

The Zipf-PSS distribution is a Poisson Stopped-Sum with a Zipf distribution as secondary distribution. In this work, we consider two INAR(1) processes: The Zipf-PSS-INAR(1) innovations process, whose innovations follow a Zipf-PSS distribution, and the Zipf-PSS-INAR(1) marginal process, whose stationary marginal distribution is Zipf-PSS. Working with the marginal process is more complex...