Core Concepts
This paper introduces a novel training method for deep probabilistic time series forecasting that improves accuracy and uncertainty quantification by explicitly modeling error autocorrelation within mini-batches using a dynamic, weighted sum of kernel matrices.
Stats
The proposed method achieves an average improvement of 8.80% for DeepAR and 9.76% for Transformer in CRPS.
The improvement in 0.9-risk (12.36%) is greater than the improvement in 0.5-risk (7.42%) when using the proposed method with DeepAR.
Quotes
"In time series analysis, errors can exhibit correlation for various reasons, such as the omission of essential covariates or model inadequacy."
"Modeling error autocorrelation is an important field in the statistical analysis of time series."
"By explicitly modeling dynamic error covariance, our method enhances training flexibility, improves time series prediction accuracy, and provides high-quality uncertainty quantification."