Core Concepts
Castor is a novel shapelet-based time series classification algorithm that outperforms state-of-the-art methods in accuracy and computational efficiency.
Abstract
The content introduces Castor, a time series classification algorithm utilizing shapelets organized into groups for diverse feature representation. It competes over temporal contexts, resulting in accurate classifiers. Castor's key features include minimal and maximal distance aggregation, occurrence counting, and z-normalization. The algorithm incorporates first-order differences for enhanced predictive performance. Experimental results show Castor's superiority over MultiRocket, Hydra, Rocket, DST, DrCif, MrSeql, UST, and z-time in accuracy and computational efficiency.
- Introduction to time series analysis tasks.
- Shapelets as discriminative subsequences.
- Castor's approach to time series transformation using shapelets.
- Features extracted from competing shapelets: minimal distance, maximal distance, occurrence.
- Subsequence normalization through z-normalization.
- Incorporation of first-order differences for improved performance.
- Computational complexity analysis of Castor.
- Experimental evaluation on UCR datasets showcasing Castor's superior accuracy and efficiency compared to existing methods.
Stats
We propose Castor as a simple and efficient time series classification algorithm that outperforms state-of-the-art classifiers significantly.
Castor utilizes g = 128 groups with k = 16 shapelets each for diverse feature representation.
Parameters include ρlower = 0.01, ρupper = 0.2 for occurrence thresholds and ρnorm = 0.5 for z-normalization probability.
Quotes
"Castor yields transformations resulting in significantly more accurate classifiers than several state-of-the-art classifiers."
"Utilizing the same number of features as comparable classifiers such as Hydra, Castor demonstrates superior runtime efficiency."