Adaptive Law-Based Transformation (ALT): A Lightweight Feature Representation for Time Series Classification

TL;DR

The paper introduces Adaptive Law-Based Transformation (ALT), a novel feature transformation method for time series classification that captures patterns of varying lengths, improving accuracy and robustness with minimal hyperparameters.

Contribution

ALT extends previous linear law-based transformation by incorporating variable-length shifted windows, enabling better handling of complex, variable-length patterns in time series data.

Findings

Achieves state-of-the-art classification accuracy.

Provides a fast and transparent feature transformation.

Requires only a few hyperparameters.

Abstract

Time series classification (TSC) is fundamental in numerous domains, including finance, healthcare, and environmental monitoring. However, traditional TSC methods often struggle with the inherent complexity and variability of time series data. Building on our previous work with the linear law-based transformation (LLT) - which improved classification accuracy by transforming the feature space based on key data patterns - we introduce adaptive law-based transformation (ALT). ALT enhances LLT by incorporating variable-length shifted time windows, enabling it to capture distinguishing patterns of various lengths and thereby handle complex time series more effectively. By mapping features into a linearly separable space, ALT provides a fast, robust, and transparent solution that achieves state-of-the-art performance with only a few hyperparameters.