Accurate and Efficient Multi-Channel Time Series Forecasting via Sparse Attention Mechanism

The paper introduces Li‑Net, a new architecture for multi‑channel time series forecasting that captures both linear and non‑linear dependencies among channels. Li‑Net compresses representations across sequence and channel dimensions, processes them with a configurable non‑linear module, and reconstructs forecasts while integrating a sparse Top‑K Softmax attention mechanism within a multi‑scale projection framework. Experiments on real‑world benchmarks show that Li‑Net matches state‑of‑the‑art accuracy while using less memory and achieving faster inference, with ablation studies confirming the contribution of each component.

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• rag
• multimodal
• benchmarks