Synthetic Time Series Generation (TSG) is crucial in a range of applications,
including data augmentation, anomaly detection, and privacy preservation.
Although significant strides have been made in this field, existing methods
exhibit three key limitations: (1) They often benchmark against similar model
types, constraining a holistic view of performance capabilities. (2) The use of
specialized synthetic and private datasets introduces biases and hampers
generalizability. (3) Ambiguous evaluation measures, often tied to custom
networks or downstream tasks, hinder consistent and fair comparison.
To overcome these limitations, we introduce \textsf{TSGBench}, the inaugural
TSG Benchmark, designed for a unified and comprehensive assessment of TSG
methods. It comprises three modules: (1) a curated collection of publicly
available, real-world datasets tailored for TSG, together with a standardized
preprocessing pipeline; (2) a comprehensive evaluation measures suite including
vanilla measures, new distance-based assessments, and visualization tools; (3)
a pioneering generalization test rooted in Domain Adaptation (DA), compatible
with all methods. We have conducted extensive experiments across ten real-world
datasets from diverse domains, utilizing ten advanced TSG methods and twelve
evaluation measures, all gauged through \textsf{TSGBench}. The results
highlight its remarkable efficacy and consistency. More importantly,
\textsf{TSGBench} delivers a statistical breakdown of method rankings,
illuminating performance variations across different datasets and measures, and
offering nuanced insights into the effectiveness of each method.

合成时间序列生成是数据增强、异常检测和隐私保护等各种应用中至关重要的，然而现有方法存在三个限制，而为了克服这些限制，我们引入了一个统一和综合评估 TSG 方法的 TSGBench 基准，它包括三个模块：一系列经过精心筛选的适用于合成时间序列的真实世界数据集和标准化的预处理流程；包括传统评估方法、基于距离的新评估方式和可视化工具在内的一套全面的评估措施；一项具有开创性的领域适应根基的泛化测试，兼容所有方法。我们对来自不同领域的十个真实世界数据集进行了广泛的实验，利用了十种先进的 TSG 方法和十二种评价措施，所有这些通过 TSGBench 进行了评估，结果突显出其显著的功效和一致性，更重要的是，TSGBench 提供了方法排名的统计细分，阐明了不同数据集和评估措施之间的性能变化，并且提供了对每种方法有效性的细致见解。