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

arXiv 2501.09217 ↗First posted 16 Jan 2025cs.LG cs.AI cs.CVcited by 0

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Abstract (arXiv)

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.

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References · 18

  1. 2018Deep learning for time series classification: a reviewcited by 575
  2. 2018The UCR Time Series Archivecited by 421
  3. 2019ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernelscited by 336
  4. 2020Benchmarking Multivariate Time Series Classification Algorithmscited by 145
  5. 2018Multivariate LSTM-FCNs for Time Series Classificationcited by 120
  6. 2021HIVE-COTE 2.0: a new meta ensemble for time series classificationcited by 118
  7. 2022Evaluation of post-hoc interpretability methods in time-series classificationcited by 53
  8. 2022HYDRA: Competing convolutional kernels for fast and accurate time series classificationcited by 53
  9. 2014Highly comparative feature-based time-series classificationcited by 39
  10. 2022Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classificationcited by 39
  11. 2023NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretrainingcited by 20
  12. 2023A Deep Dive into Perturbations as Evaluation Technique for Time Series XAIcited by 11
6 more
  1. 2022Reconstruction of observed mechanical motions with Artificial Intelligence toolscited by 7
  2. 2023MTS2Graph: Interpretable Multivariate Time Series Classification with Temporal Evolving Graphscited by 7
  3. 2023LB-SimTSC: An Efficient Similarity-Aware Graph Neural Network for Semi-Supervised Time Series Classificationcited by 3
  4. 2023LLT: An R package for Linear Law-based Feature Space Transformationcited by 3
  5. 2023Learning ECG Signal Features Without Backpropagation Using Linear Lawscited by 2
  6. 2024Sensor-Aware Classifiers for Energy-Efficient Time Series Applications on IoT Devicescited by 2

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