The imputation of missing values in multivariate time series (MTS) data is critical in ensuring
data quality and producing reliable data-driven predictive models. Apart from many …

The ubiquitous missing values cause the multivariate time series data to be partially
observed, destroying the integrity of time series and hindering the effective time series data …

Data are naturally collected in their raw state and must undergo a series of preprocessing
steps to obtain data in their input state for Artificial Intelligence (AI) and other applications …

We present DeepMVI, a deep learning method for missing value imputation in
multidimensional time-series datasets. Missing values are commonplace in decision support …

Time series data, including univariate and multivariate ones, are characterized by unique
composition and complex multi-scale temporal variations. They often require special …

Effective imputation is a crucial preprocessing step for time series analysis. Despite the
development of numerous deep learning algorithms for time series imputation, the …

Time series databases are essential for the large-scale deployment of many critical
industrial applications. In infrastructure monitoring, for instance, a database system should …

Over the past few years, the scale of sensor networks has greatly expanded. This generates
extended spatiotemporal datasets, which form a crucial information resource in numerous …

Deep representation learning has been widely explored for decoding motor imagery
electroencephalogram (MI-EEG) to build EEG-tailored brain-computer interfaces. Due to the …