Multivariate Probabilistic Time Series Forecasting with Correlated Errors

no code implementations • 1 Feb 2024 • Vincent Zhihao Zheng, Lijun Sun

Accurately modeling the correlation structure of errors is essential for reliable uncertainty quantification in probabilistic time series forecasting.

Computational Efficiency Probabilistic Time Series Forecasting +2

Better Batch for Deep Probabilistic Time Series Forecasting

no code implementations • 26 May 2023 • Vincent Zhihao Zheng, Seongjin Choi, Lijun Sun

Deep probabilistic time series forecasting has gained attention for its ability to provide nonlinear approximation and valuable uncertainty quantification for decision-making.

Decision Making Probabilistic Time Series Forecasting +2

Enhancing Deep Traffic Forecasting Models with Dynamic Regression

no code implementations • 17 Jan 2023 • Vincent Zhihao Zheng, Seongjin Choi, Lijun Sun

Deep learning models for traffic forecasting often assume the residual is independent and isotropic across time and space.

regression Time Series +1

Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting

no code implementations • 10 Dec 2022 • Seongjin Choi, Nicolas Saunier, Vincent Zhihao Zheng, Martin Trepanier, Lijun Sun

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.