5 accepted papers
Missing values in multivariate time series data present significant challenges to effective analysis. Existing methods for multivariate time series analysis either ignore missing data, sacrificing performance, or follow the impute-then-analyze paradigm, which suffers from redundant training and erro
Multivariate time series (MTS) data in real-world scenarios are often incomplete, which hinders effective data analysis. Therefore, MTS imputation has been widely studied to facilitate various MTS tasks. Existing imputation methods primarily initialize missing values with zeros in order to perform e
The widespread presence of incomplete modalities in multimodal data poses a significant challenge to achieving accurate rumor detection. Existing multimodal rumor detection methods primarily focus on learning joint modality representations from complete multimodal training data, rendering them ineff
The multi-view data with incomplete information hinder the effective data analysis. Existing multi-view imputation methods that learn the mapping between complete view and completely missing view are not able to deal with the common multi-view data with missing feature information. In this paper, we…
The missing values, widely existed in multivariate time series data, hinder the effective data analysis. Existing time series imputation methods do not make full use of the label information in real-life time series data. In this paper, we propose a novel semi-supervised generative adversarial netwo…