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.

翻译：线性定律特征空间变换（LLT）算法的目标是辅助单变量和多变量时间序列的分类。本文介绍的名为LLT的R包，以灵活且用户友好的方式实现了该算法。该包首先将样本划分为训练集和测试集，然后利用时延嵌入和谱分解技术识别训练集中每个输入序列（初始特征）的控制模式（称为线性定律），最后应用训练集的线性定律对测试集的初始特征进行变换。这些步骤通过三个独立函数完成，分别名为trainTest、trainLaw和testTrans。它们的应用需要预定义的数据结构，但为了快速计算，只使用内置函数。LLT的R包及具有适当数据结构的示例数据集已在GitHub上公开。