KEMM-Net enriches power load time series representations with cross-modal text and visual knowledge via PID-guided contrastive learning to outperform baselines in few-shot forecasting scenarios.
Visionts: Vi- sual masked autoencoders are free-lunch zero-shot time series forecast- ers
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Beyond Information Redundancy: Expanding Cross-Modal Knowledge Representation for Power Load Time Series Forecasting
KEMM-Net enriches power load time series representations with cross-modal text and visual knowledge via PID-guided contrastive learning to outperform baselines in few-shot forecasting scenarios.