Better Batch for Deep Probabilistic Time Series Forecasting
This work addresses a specific limitation in time series forecasting for applications requiring accurate uncertainty quantification, representing an incremental advancement.
The paper tackles the problem of oversimplified error processes in deep probabilistic time series forecasting by proposing a training method that incorporates error autocorrelation to enhance accuracy. Experimental results show notable improvements in predictive accuracy across multiple datasets and models.
Deep probabilistic time series forecasting has gained attention for its ability to provide nonlinear approximation and valuable uncertainty quantification for decision-making. However, existing models often oversimplify the problem by assuming a time-independent error process and overlooking serial correlation. To overcome this limitation, we propose an innovative training method that incorporates error autocorrelation to enhance probabilistic forecasting accuracy. Our method constructs a mini-batch as a collection of $D$ consecutive time series segments for model training. It explicitly learns a time-varying covariance matrix over each mini-batch, encoding error correlation among adjacent time steps. The learned covariance matrix can be used to improve prediction accuracy and enhance uncertainty quantification. We evaluate our method on two different neural forecasting models and multiple public datasets. Experimental results confirm the effectiveness of the proposed approach in improving the performance of both models across a range of datasets, resulting in notable improvements in predictive accuracy.