# LLT: An R package for Linear Law-based Feature Space Transformation

arXiv: https://arxiv.org/abs/2304.14211 · HTML: https://mlsanity.com/paper/2304.14211/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 46 references that are in the map.
- First posted on arXiv 27 Apr 2023.

## Builds on (most cited first, 9 of 9)

- [Benchmarking Multivariate Time Series Classification Algorithms](https://mlsanity.com/paper/2007.13156/) (2020)
- [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017)
- [Multivariate LSTM-FCNs for Time Series Classification](https://mlsanity.com/paper/1801.04503/) (2018)
- [Multivariate Time Series Classification with WEASEL+MUSE](https://mlsanity.com/paper/1711.11343/) (2017)
- [Evaluating data augmentation for financial time series classification](https://mlsanity.com/paper/2010.15111/) (2020)
- [Reconstruction of observed mechanical motions with Artificial Intelligence tools](https://mlsanity.com/paper/2202.11447/) (2022)
- [Time series analysis with dynamic law exploration](https://mlsanity.com/paper/2104.10970/) (2021)
- [A Generative Modeling Approach to Limited Channel ECG Classification](https://mlsanity.com/paper/1802.06458/) (2018)
- [Linear Laws of Markov Chains with an Application for Anomaly Detection in Bitcoin Prices](https://mlsanity.com/paper/2201.09790/) (2022)

## Built on by (most cited first, 3 of 3)

- [Learning ECG Signal Features Without Backpropagation Using Linear Laws](https://mlsanity.com/paper/2307.01930/) (2023)
- [Adaptive Law-Based Transformation (ALT): A Lightweight Feature Representation for Time Series Classification](https://mlsanity.com/paper/2501.09217/) (2025)
- [ALT: A Python Package for Lightweight Feature Representation in Time Series Classification](https://mlsanity.com/paper/2504.12841/) (2025)

## Abstract (arXiv)

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.