LLT: An R package for Linear Law-based Feature Space Transformation
This work provides a tool for time series classification, but it appears incremental as it packages an existing algorithm into an R implementation.
The authors introduced an R package called LLT that implements a linear law-based feature space transformation algorithm to assist with classifying univariate and multivariate time series, utilizing time-delay embedding and spectral decomposition to identify patterns and transform features.
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.