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Irregular time series forecasting can be revolutionized with DNBNet, which eliminates bias and adapts to diverse temporal patterns for superior predictive performance.
Relying on MSE for irregular time-series forecasting can lead to misleading evaluations, as it fails to account for timestamp sampling biases.
GLAIM achieves state-of-the-art imputation performance by seamlessly combining stable global and adaptive local inter-variable dependencies, setting a new benchmark for handling multivariate time series with missing data.
