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）算法的目标在于辅助单变量与多变量时间序列的分类任务。本文介绍的R包LLT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，随后利用时延嵌入与谱分解技术识别训练集中每个输入序列（初始特征）的支配性模式（称为线性定律），最终应用训练集的线性定律对测试集的初始特征进行变换。这三个步骤由三个独立函数（trainTest、trainLaw、testTrans）执行。这些函数的调用需预定义数据结构，但为加速计算仅使用内置函数。LLT R包及包含相应数据结构的示例数据集已在GitHub上公开提供。