  Smart contracts are autonomous software executing predefined conditions. Two
of the biggest advantages of the smart contracts are secured protocols and
transaction costs reduction. On the Ethereum platform, an open-source
blockchain-based platform, smart contracts implement a distributed virtual
machine on the distributed ledger. To avoid denial of service attacks and
monetize the services, payment transactions are executed whenever code is being
executed between contracts. It is thus natural to investigate if predictive
analysis is capable to forecast these interactions. We have addressed this
issue and propose an innovative application of the tensor decomposition
CANDECOMP/PARAFAC to the temporal link prediction of smart contracts. We
introduce a new approach leveraging stochastic processes for series predictions
based on the tensor decomposition that can be used for smart contracts
predictive analytics.
