1.0Microsoft Researchhttps://www.microsoft.com/en-us/researchChengyun Qiuhttps://www.microsoft.com/en-us/research/people/v-cheqi/Towards Editing Time Series - Microsoft Researchrich600338<blockquote class="wp-embedded-content" data-secret="kUi1MInFr6"><a href="https://www.microsoft.com/en-us/research/publication/towards-editing-time-series/">Towards Editing Time Series</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/towards-editing-time-series/embed/#?secret=kUi1MInFr6" width="600" height="338" title="“Towards Editing Time Series” — Microsoft Research" data-secret="kUi1MInFr6" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript">
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Synthesizing time series data is pivotal in modern society, aiding effective decision making and ensuring privacy preservation in various scenarios. Time series are associated with various attributes, including trends, seasonality, and external information such as location. Recent research has predominantly focused on random unconditional synthesis or conditional synthesis. Nonetheless, these paradigms generate time series from […]