Adaptive Law-Based Transformation (ALT): A Lightweight Feature Representation for Time Series Classification
This addresses the challenge of handling complex and variable time series data in domains like finance and healthcare, but it is incremental as it builds on prior work.
The paper tackles the problem of time series classification by introducing adaptive law-based transformation (ALT), which enhances a previous method with variable-length shifted time windows to capture patterns of various lengths, resulting in state-of-the-art performance with a fast and robust solution.
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.