Temporally indexed data are essential in a wide range of fields and of interest to machine learning researchers. Time series data, however, are often scarce or highly sensitive, which precludes the sharing of data between researchers and industrial organizations and the application of existing and new data-intensive ML methods. A possible solution to this bottleneck is to generate synthetic data. In this work, we introduce Time Series Generative Modeling (TSGM), an open-source framework for the generative modeling of synthetic time series. TSGM includes a broad repertoire of machine learning methods: generative models, probabilistic, and simulator-based approaches. The framework enables users to evaluate the quality of the produced data from different angles: similarity, downstream effectiveness, predictive consistency, diversity, and privacy. The framework is extensible, which allows researchers to rapidly implement their own methods and compare them in a shareable environment. TSGM was tested on open datasets and in production and proved to be beneficial in both cases. Additionally to the library, the project allows users to employ command line interfaces for synthetic data generation which lowers the entry threshold for those without a programming background.

翻译：时间索引数据在众多领域中至关重要，并受到机器学习研究者的关注。然而，时间序列数据往往稀缺或高度敏感，这阻碍了研究者与工业组织间的数据共享，以及现有和新兴数据密集型机器学习方法的应用。解决这一瓶颈的可能方案是生成合成数据。本文介绍时间序列生成建模（TSGM），一个用于合成时间序列生成建模的开源框架。TSGM包含广泛的机器学习方法：生成模型、概率方法和基于模拟器的途径。该框架使用户能够从不同角度评估生成数据的质量：相似性、下游有效性、预测一致性、多样性和隐私性。框架具有可扩展性，使研究者能够快速实现自身方法，并在可共享环境中进行对比。TSGM已在开放数据集和生产环境中测试，并在两种场景下均展现出优势。除库外，该项目还允许用户通过命令行界面进行合成数据生成，降低了无编程背景用户的入门门槛。