  Smart contracts are programs stored and executed on a blockchain. The
Ethereum platform, an open-source blockchain-based platform, has been designed
to use these programs offering secured protocols and transaction costs
reduction. The Ethereum Virtual Machine performs smart contracts runs, where
the execution of each contract is limited to the amount of gas required to
execute the operations described in the code. Each gas unit must be paid using
Ether, the crypto-currency of the platform. Due to smart contracts interactions
evolving over time, analyzing the behavior of smart contracts is very
challenging. We address this challenge in our paper. We develop for this
purpose an innovative approach based on the non-negative tensor decomposition
PARATUCK2 combined with long short-term memory (LSTM) to assess if predictive
analysis can forecast smart contracts interactions over time. To validate our
methodology, we report results for two use cases. The main use case is related
to analyzing smart contracts and allows shedding some light into the complex
interactions among smart contracts. In order to show the generality of our
method on other use cases, we also report its performance on video on demand
recommendation.
