18 citations · 19 across the 7 of their papers we have counts for
10 papers
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…