TSGM: A Flexible Framework for Generative Modeling of Synthetic Time
  Series
Alexander Nikitin Letizia Iannucci Samuel Kaski
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.