  The aim of this paper is to investigate the effect of a novel method called
linear law-based feature space transformation (LLT) on the accuracy of intraday
price movement prediction of cryptocurrencies. To do this, the 1-minute
interval price data of Bitcoin, Ethereum, Binance Coin, and Ripple between 1
January 2019 and 22 October 2022 were collected from the Binance cryptocurrency
exchange. Then, 14-hour nonoverlapping time windows were applied to sample the
price data. The classification was based on the first 12 hours, and the two
classes were determined based on whether the closing price rose or fell after
the next 2 hours. These price data were first transformed with the LLT, then
they were classified by traditional machine learning algorithms with 10-fold
cross-validation. Based on the results, LLT greatly increased the accuracy for
all cryptocurrencies, which emphasizes the potential of the LLT algorithm in
predicting price movements.
