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Found 4 papers, 0 papers with code
Multivariate Probabilistic Time Series Forecasting with Correlated Errors
Modeling the correlations among errors is closely associated with how accurately the model can quantify predictive uncertainty in probabilistic time series forecasting.
Better Batch for Deep Probabilistic Time Series Forecasting
Our method constructs a mini-batch as a collection of $D$ consecutive time series segments for model training.
Enhancing Deep Traffic Forecasting Models with Dynamic Regression
A common assumption in deep learning-based multivariate and multistep traffic time series forecasting models is that residuals are independent, isotropic, and uncorrelated in space and time.
Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting
Deep learning-based multivariate and multistep-ahead traffic forecasting models are typically trained with the mean squared error (MSE) or mean absolute error (MAE) as the loss function in a sequence-to-sequence setting, simply assuming that the errors follow an independent and isotropic Gaussian or Laplacian distributions.
