Compares foundation models for probabilistic low-voltage load forecasting on 200 real feeders and introduces a grid-planning metric that scores peak prediction by its effect on asset cost-risk decisions.
Tsmixer: Lightweight mlp-mixer model for multivariate time series forecasting, in: Proceedings of the 29th ACM SIGKDD ConferenceonKnowledgeDiscoveryandDataMining,p.459–469
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 4roles
background 1polarities
background 1representative citing papers
Gated Multi-modal Fusion reaches 0.82 macro F1 on HARMES, beating the concatenation baseline of 0.76 by 6 points under leave-one-participant-out evaluation.
SpecReTF improves time series forecasting by retrieving similar historical patterns using windowed frequency representations with combined amplitude-phase similarity and exponential recency weighting, outperforming time-domain methods on benchmarks.
This is the first comprehensive survey of OOD generalization methodologies for time series, organized across data distribution, representation learning, and OOD evaluation.
citing papers explorer
-
Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics
Compares foundation models for probabilistic low-voltage load forecasting on 200 real feeders and introduces a grid-planning metric that scores peak prediction by its effect on asset cost-risk decisions.
-
A Comparison of Fusion Techniques for Multi-Modal Human Activity Recognition on the HARMES Dataset
Gated Multi-modal Fusion reaches 0.82 macro F1 on HARMES, beating the concatenation baseline of 0.76 by 6 points under leave-one-participant-out evaluation.
-
Spectral Retrieval-Augmented Time-Series Forecasting
SpecReTF improves time series forecasting by retrieving similar historical patterns using windowed frequency representations with combined amplitude-phase similarity and exponential recency weighting, outperforming time-domain methods on benchmarks.
-
Out-of-Distribution Generalization in Time Series: A Survey
This is the first comprehensive survey of OOD generalization methodologies for time series, organized across data distribution, representation learning, and OOD evaluation.