Rapid Augmentations for Time Series (RATS): A High-Performance Library for Time Series Augmentation

TL;DR

RATS is a high-performance Rust-based library with Python bindings that significantly accelerates time series data augmentation, enabling large-scale deep learning applications with reduced memory footprint.

Contribution

The paper introduces RATS, a fast, memory-efficient time series augmentation library in Rust with Python bindings, outperforming existing tools in speed and scalability.

Findings

Achieves an average of 74.5% speedup over tsaug.

Reduces peak memory usage by up to 47.9%.

Effective on large-scale datasets with improved performance.

Abstract

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.