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.

本研究旨在探究一种新的方法 -- 线性法特征空间转换 (LLT) 对加密货币日内价格运动预测准确性的影响。通过改变数据进行分类和机器学习算法的交叉验证，LLT 算法极大地提高了所有加密货币的预测准确性。

利用基于线性法的转换预测加密货币的价格走势

Predicting the Price Movement of Cryptocurrencies Using Linear Law-based  Transformation

The goal of the linear law-based feature space transformation (LLT) algorithm
is to assist with the classification of univariate and multivariate time
series. The presented R package, called LLT, implements this algorithm in a
flexible yet user-friendly way. This package first splits the instances into
training and test sets. It then utilizes time-delay embedding and spectral
decomposition techniques to identify the governing patterns (called linear
laws) of each input sequence (initial feature) within the training set.
Finally, it applies the linear laws of the training set to transform the
initial features of the test set. These steps are performed by three separate
functions called trainTest, trainLaw, and testTrans. Their application requires
a predefined data structure; however, for fast calculation, they use only
built-in functions. The LLT R package and a sample dataset with the appropriate
data structure are publicly available on GitHub.

该研究提出了一种基于线性法则的特征空间转换（LLT）算法，用于辅助单变量和多变量时间序列分类。LLT R 软件包可以将训练集中的线性法则应用于测试集，从而实现快速的特征转换。