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

2026-01-06

arXiv.org

AI Summary

This work addresses the performance bottlenecks of existing time series augmentation libraries under large-scale data scenarios, which often fail to meet production-level requirements. To overcome this limitation, we propose RATSpy—the first high-performance time series augmentation library implemented in Rust with Python bindings—introducing Rust into this domain for the first time. RATSpy integrates a comprehensive suite of augmentation techniques, including basic transformations, frequency-domain operations, and time warping, all unified under a consistent pipeline interface that enables efficient parallel computation. Extensive experiments across 143 datasets demonstrate that RATSpy achieves an average speedup of 74.5% (up to 94.8%) compared to tsaug, while reducing peak memory consumption by up to 47.9%, thereby significantly enhancing processing efficiency and resource utilization in large-scale applications.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• time series augmentation
• performance bottleneck
• scalability
• large-scale systems
• computational efficiency

Innovation

• time series augmentation
• high-performance computing
• Rust
• parallelization
• memory efficiency