An LLM-based agentic sampler over building knowledge graphs selects target-specific exogenous variables for zero-shot IoT forecasting, matching or beating trained baselines on three real buildings.
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MAP4TS combines global, local, statistical, and temporal prompts derived from classical time-series analysis with raw embeddings via cross-modality alignment to improve LLM forecasting performance across eight datasets.
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TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting
An LLM-based agentic sampler over building knowledge graphs selects target-specific exogenous variables for zero-shot IoT forecasting, matching or beating trained baselines on three real buildings.
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MAP4TS: A Multi-Aspect Prompting Framework for Time-Series Forecasting with Large Language Models
MAP4TS combines global, local, statistical, and temporal prompts derived from classical time-series analysis with raw embeddings via cross-modality alignment to improve LLM forecasting performance across eight datasets.