RATS库用Rust实现，让时间序列增强提速近75%且更省内存。
Rapid Augmentations for Time Series (RATS): A High-Performance Library for Time Series Augmentation
- 用Rust重写增强库，支持并行处理统一接口。
- 在143个数据集上平均快74.5%，大样本下最高提速94.8%。
- 适合需要高效训练的工业级时序模型开发者。
时间序列增强对训练鲁棒的深度学习模型至关重要，尤其在标签数据稀缺且获取成本高的领域。然而，现有主要基于Python的时间序列增强库存在性能瓶颈，随着数据规模增大，运行时间呈指数增长，限制了其在大规模生产系统中的应用。本文提出RATS（Rapid Augmentations for Time Series），一个使用Rust编写的高性能时间序列增强库，提供Python绑定（RATSpy）。RATS实现了多种增强方法，涵盖基础变换、频域操作和时间扭曲技术，均通过统一管道接口调用，并内置并行化支持。在143个数据集上对RATSpy与常用库tsaug进行综合基准测试显示，RATSpy平均提速74.5%（大数据集最高达94.8%），峰值内存占用降低最多47.9%。
原文摘要 · Abstract (English)
Time series augmentation is critical for training robust deep learning models, particularly in domains where labelled data is scarce and expensive to obtain. However, existing augmentation libraries for time series, mainly written in Python, suffer from performance bottlenecks, where running time grows exponentially as dataset sizes increase -- an aspect limiting their applicability in large-scale, production-grade systems. We introduce RATS (Rapid Augmentations for Time Series), a high-performance library for time series augmentation written in Rust with Python bindings (RATSpy). RATS implements multiple augmentation methods spanning basic transformations, frequency-domain operations and time warping techniques, all accessible through a unified pipeline interface with built-in parallelisation. Comprehensive benchmarking of RATSpy versus a commonly used library (tasug) on 143 datasets demonstrates that RATSpy achieves an average speedup of 74.5\% over tsaug (up to 94.8\% on large datasets), with up to 47.9\% less peak memory usage.
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