Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning
  Methods
Conall Butler Martin Crane
Gas is the transaction-fee metering system of the Ethereum network. Users of
the network are required to select a gas price for submission with their
transaction, creating a risk of overpaying or delayed/unprocessed transactions
in this selection. In this work, we investigate data in the aftermath of the
London Hard Fork and shed insight into the transaction dynamics of the net-work
after this major fork. As such, this paper provides an update on work previous
to 2019 on the link between EthUSD BitUSD and gas price. For forecasting, we
compare a novel combination of machine learning methods such as Direct
Recursive Hybrid LSTM, CNNLSTM, and Attention LSTM. These are combined with
wavelet threshold denoising and matrix profile data processing toward the
forecasting of block minimum gas price, on a 5-min timescale, over multiple
lookaheads. As the first application of the matrix profile being applied to gas
price data and forecasting we are aware of, this study demonstrates that matrix
profile data can enhance attention-based models however, given the hardware
constraints, hybrid models outperformed attention and CNNLSTM models. The
wavelet coherence of inputs demonstrates correlation in multiple variables on a
1 day timescale, which is a deviation of base free from gas price. A
Direct-Recursive Hybrid LSTM strategy outperforms other models. Hybrid models
have favourable performance up to a 20 min lookahead with performance being
comparable to attention models when forecasting 25/50-min ahead. Forecasts over
a range of lookaheads allow users to make an informed decision on gas price
selection and the optimal window to submit their transaction in without fear of
their transaction being rejected. This, in turn, gives more detailed insight
into gas price dynamics than existing recommenders, oracles and forecasting
approaches, which provide simple heuristics or limited lookahead horizons.