Prediction of Ether Prices Using DeepAR and Probabilistic Forecasting

EasyChair Preprint 8274

Abstract

Ethereum is a major public blockchain. Besides being the second-largest digital currency by market capitalization for its cryptocurrency, the Ether ETH, it is also the foundation of Web3 and decentralized applications, or DApps, that are fueled by Smart Contracts. Ethereum uses Proof of Work (PoW) consensus algorithm to ensure the integrity of the blockchain and to prevent double spend. PoW requires the participation of miners, who are incentivized to assemble blocks of transactions by being rewarded with cryptocurrency paid by transaction originators and by the blockchain network itself via newly minted ETH. Network fees for transaction submissions are called gas, by analogy to the fuel used by cars, and are negotiable. They are also highly volatile and hence it is critical to predict the direction they are heading into, so that one can time transaction submissions, when feasible. There have been several efforts to predict gas prices, including usage of large Mempools, analysis of committed blocks, and more recent ones using Facebook's Prophet model~\cite{prophet}. Our solution uses DeepAR, a model built on recurrent neural networks (RNN) with the ability leverage hundreds of related time series to produce more accurate predictions. Our implementation uses features extracted from the Ethereum main net as well as off-chain data to achieve accurate predictions.

Keyphrases: Blockchain, DeepAR, Ethereum, Gas Price, machine learning, probabilistic forecasting

@booklet{EasyChair:8274,
  author    = {Andras Ferenczi and Costin Badica},
  title     = {Prediction of Ether Prices Using DeepAR and Probabilistic Forecasting},
  howpublished = {EasyChair Preprint 8274},
  year      = {EasyChair, 2022}}