Blockchain-Assisted Temporal Graph Learning for Trustworthy Supply Chain Forecasting: A Bangladesh Case Study

1)A blockchain-backed provenance mechanism for temporal
supply-chain data, using an off-chain-data / on-chain-hash
design with a minimal audited Solidity contract;
2) Integration of verified temporal data with graph-based
production forecasting (persistence, LSTM, and GCon
vLSTM) under a strictly chronological protocol;
3)A controlled data-tampering experiment that quantifies
how tampering degrades forecasting; and
4) Quantification of tamper detection, verification-driven re
covery, and the provenance-layer overhead.