Prediction Inference of Time Series with Standard ReLU Deep Neural Networks
We propose a methodology based on the standard ReLU Deep Neural Networks (DNN) to make predictions and quantify their uncertainty. Classically, people rely on linear, non-linear, or non-parametric kernel methods to fit and then predict the time series. As the universal approximation ability was revealed for DNN, its application has become more and more popular for prediction tasks in various scientific areas. However, the corresponding uncertainty quantification has not been studied thoroughly. Particularly, the uncertainty in prediction will consist of two parts: (1) the future variability; (2) the estimation variability within training data. To capture both variabilities, we build the s...
| Authors | Kejin Wu |
|---|---|
| Primary category | stat.ML |
| Published | 15 August 2026 |
| Last updated at source | 15 August 2026 |
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