arXiv:2311.14549
공식 코드 · Papers with Code
FRUITS: Feature Extraction Using Iterated Sums for Time Series Classification
원문 초록
We introduce a pipeline for time series classification that extracts features based on the iterated-sums signature (ISS) and then applies a linear classifier. These features are intrinsically nonlinear, capture chronological information, and, under certain settings, are invariant to time-warping. We are competitive with state-of-the-art methods on the UCR archive, both in terms of accuracy and speed. We make our code available at \url{https://github.com/irkri/fruits}.
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TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning
FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series Classification
Implet: A Post-hoc Subsequence Explainer for Time Series Models
Learning Soft Sparse Shapes for Efficient Time-Series Classification
PYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks
Lightweight Defense Against Adversarial Attacks in Time Series Classification
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