Inside nnfee: Estimating Bitcoin Fees Via a Neural Network
At the confluence of machine learning and the cryptoverse is nnfee — an “attempt at estimating bitcoin fees with neural networks.” Bitsonline talked with nnfee’s creator, Redditor u/mess110, to see what the tech might be applied for and how its accuracy might be improved further.
A New Way to Estimate Fees?
Devised by Redditor u/mess110, the project is seemingly making headway into uncharted waters. To date, one or two machine learning (ML) marketplace plays have cropped up in the cryptoverse, but u/mess110’s singular exploratory experiment would appear to be the first undertaking, or among the firsts, aimed at predicting bitcoin transaction fees.
The Redditor, who described themself as a “free software developer” and “bitcoin contributor,” started with ML’s “Iris classification problem” and then “tried to model my data accordingly.”
Training the Network
In modeling nnfee, u/mess110 used bitcoin block and transaction data from between March 2017 and February 2018 — exactly 12 months. As such, the total amount of data collected from this period totaled up to 115 GB. The developer saved both full and slim versions of each block, the latter versions being for the purpose of quick searches later.
They used Smartbit’s API for “returning all the blocks, transactions, when the transaction was first seen, and when it was confirmed,” and they also used Johoe’s Bitcoin Mempool Statistics tracker to collect all relevant mempool information for the period in question.
With all of this data, u/mess110 then separated the training into 12 parts according to each of the 12 months that were respectively tracked. The developer clarified that 5 percent “of the transactions from each month were randomly selected to verify the neural network and were not used for training.”
The end result? 71.8 percent accuracy at bitcoin fee estimation, a number that u/mess110 notes is “better than random.”
Fine-Tuning and Looking Ahead
I asked u/mess110 if they thought nnfee’s accuracy could be improved and how, and they were cautiously optimistic on the possibility and hoped to hear from others in the field:
“In terms of AI, I have experience, but I wish I knew more. Someone more specialized in AI could probably suggest a different neural network or layer configuration which would improve the 71 percent accuracy. I am sort of hoping I get some feedback on this from the community.”
I also asked the developer what they envisioned the future general applicability of such a network might be. They answered:
“If [these networks] become reliable enough, people could forget about the fee concept completely. It would be handled by AI in the background. Not 100 percent sure we want that though, needs some debate. It could maybe help smooth the experience for new users because they don’t need to know about fees from the start and know their transaction will be confirmed at the correct fee.”
The developer said they’d like to try another round at training an nnfee-like system, though next time with less training data, concluding they “really want to explore” further but that “it needs testing.”
What’s your take? Can you envision any other applications for neural networks in the cryptoverse? Let us know what you think in the comments below.
Images via Becoming Human, Risk