English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Temporally indexed data are essential in a wide range of fields and ofinterest to machine learning researchers. Time series data, however, are oftenscarce or highly sensitive, which precludes the sharing of data betweenresearchers and industrial organizations and the application of existing andnew data-intensive ML methods. A possible solution to this bottleneck is togenerate synthetic data. In this work, we introduce Time Series GenerativeModeling (TSGM), an open-source framework for the generative modeling ofsynthetic time series. TSGM includes a broad repertoire of machine learningmethods: generative models, probabilistic, and simulator-based approaches. Theframework enables users to evaluate the quality of the produced data fromdifferent angles: similarity, downstream effectiveness, predictive consistency,diversity, and privacy. The framework is extensible, which allows researchersto rapidly implement their own methods and compare them in a shareableenvironment. TSGM was tested on open datasets and in production and proved tobe beneficial in both cases. Additionally to the library, the project allowsusers to employ command line interfaces for synthetic data generation whichlowers the entry threshold for those without a programming background.

TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series