SEED is an LLM-based forecasting architecture that couples variable-wise attention encoding with prototype-based semantic reprogramming and a frozen decoder.
An arima-lstm model for predicting volatile agricultural price series with random forest technique,
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SEED: A Structural Encoder for Embedding-Driven Decoding in Time Series Prediction with LLMs
SEED is an LLM-based forecasting architecture that couples variable-wise attention encoding with prototype-based semantic reprogramming and a frozen decoder.