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.
Temporal fusion transformers for interpretable multi-horizon time series forecasting,
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
StateFlow extends VARNN with dual hidden and residual-memory states plus a chunk decoder and two-stage training to enable competitive long-horizon time series forecasting while retaining a compact recurrent design.
MMPM uses PIM for gaze/head/hand interactions and MTP (CVAE with query decoder) to model separate crossing/non-crossing trajectory distributions, outperforming baselines on PIE and JAAD with a new validation protocol.
Signed Dual Attention is a parameter-free attention module that models signed dependencies in time series via dual message passing to achieve two-head expressiveness in one block.
citing papers explorer
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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.
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StateFlow: Dual-State Recurrent Modeling for Long-Horizon Time Series Forecasting
StateFlow extends VARNN with dual hidden and residual-memory states plus a chunk decoder and two-stage training to enable competitive long-horizon time series forecasting while retaining a compact recurrent design.
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Where Will They Go? Modelling Multimodal Pedestrian Manoeuvres from Ego-centric Videos
MMPM uses PIM for gaze/head/hand interactions and MTP (CVAE with query decoder) to model separate crossing/non-crossing trajectory distributions, outperforming baselines on PIE and JAAD with a new validation protocol.
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Signed Dual Attention: Capturing Signed Dependencies in Time Series Forecasting
Signed Dual Attention is a parameter-free attention module that models signed dependencies in time series via dual message passing to achieve two-head expressiveness in one block.