Self‑Supervised Temporal Pattern Mining for Circular Manufacturing Supply Chains for Extreme Data Sparsity Scenarios

In circular supply chains with extremely sparse, irregular return data, traditional forecasting fails. A self‑supervised contrastive learning framework learns r