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

Abstract

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.

arXiv comment15 pages, 5 figures, 1 table

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• Deep Learning for Sensor-based Activity Recognition: A Survey
• Multivariate LSTM-FCNs for Time Series Classification
• Benchmarking Multivariate Time Series Classification Algorithms
• Time series analysis with dynamic law exploration
• LLT: An R package for Linear Law-based Feature Space Transformation
• Predicting the Price Movement of Cryptocurrencies Using Linear Law-based Transformation

Cited by in corpus (3)

• Learning ECG Signal Features Without Backpropagation Using Linear Laws
• LLT: An R package for Linear Law-based Feature Space Transformation
• Adaptive Law-Based Transformation (ALT): A Lightweight Feature Representation for Time Series Classification