18 citations · 20 across the 10 of their papers we have counts for
11 papers
EnergyBench: a live, gate-aligned benchmark of day-ahead forecasts for European power markets
Evgenii Genov, Ander Martinez Alonso, Christoph Bergmeir
Energy forecasting models are usually compared on backtests and fixed public datasets. Backtests can use data that did not yet exist when the forecast would have been made, and a f…
Seeking SOTA: Time-Series Forecasting Must Adopt Taxonomy-Specific Evaluation to Dispel Illusory Gains
Raeid Saqur, Christoph Bergmeir, Blanka Horvath +3
We argue that the current practice of evaluating AI/ML time-series forecasting models, predominantly on benchmarks characterized by strong, persistent periodicities and seasonaliti…
Extending Load Forecasting from Zonal Aggregates to Individual Nodes for Transmission System Operators
Oskar Triebe, Fletcher Passow, Simon Wittner +9
The reliability of local power grid infrastructure is challenged by sustainable energy developments increasing electric load uncertainty. Transmission System Operators (TSOs) need…
MoTime: A Dataset Suite for Multimodal Time Series Forecasting
Xin Zhou, Weiqing Wang, Francisco J. Baldán +2
While multimodal data sources are increasingly available from real-world forecasting, most existing research remains on unimodal time series. In this work, we present MoTime, a sui…
Unveiling the Potential of Text in High-Dimensional Time Series Forecasting
Xin Zhou, Weiqing Wang, Shilin Qu +2
Time series forecasting has traditionally focused on univariate and multivariate numerical data, often overlooking the benefits of incorporating multimodal information, particularl…
Creating a Cooperative AI Policymaking Platform through Open Source Collaboration
Aiden Lewington, Alekhya Vittalam, Anshumaan Singh +48
Advances in artificial intelligence (AI) present significant risks and opportunities, requiring improved governance to mitigate societal harms and promote equitable benefits. Curre…
Scalable Transformer for High Dimensional Multivariate Time Series Forecasting
Xin Zhou, Weiqing Wang, Wray Buntine +4
Deep models for Multivariate Time Series (MTS) forecasting have recently demonstrated significant success. Channel-dependent models capture complex dependencies that channel-indepe…
Balancing Forecast Accuracy and Switching Costs in Online Optimization of Energy Management Systems
Evgenii Genov, Julian Ruddick, Christoph Bergmeir +4
This study investigates the integration of forecasting and optimization in energy management systems, with a focus on the role of switching costs -- penalties incurred from frequen…
Fast Gibbs sampling for the local and global trend Bayesian exponential smoothing model
Xueying Long, Daniel F. Schmidt, Christoph Bergmeir +1
In Smyl et al. [Local and global trend Bayesian exponential smoothing models. International Journal of Forecasting, 2024.], a generalised exponential smoothing model was proposed t…
DeepHGNN: Study of Graph Neural Network based Forecasting Methods for Hierarchically Related Multivariate Time Series
Abishek Sriramulu, Nicolas Fourrier, Christoph Bergmeir
Graph Neural Networks (GNN) have gained significant traction in the forecasting domain, especially for their capacity to simultaneously account for intra-series temporal correlatio…
Context Neural Networks: A Scalable Multivariate Model for Time Series Forecasting
Abishek Sriramulu, Christoph Bergmeir, Slawek Smyl
Real-world time series often exhibit complex interdependencies that cannot be captured in isolation. Global models that model past data from multiple related time series globally w…