Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust
This work addresses the problem of uncertainty estimation for risk management and decision-making in time series forecasting, offering a computationally efficient solution that is incremental in nature.
The paper tackles the lack of point uncertainty estimates in time series forecasting models by introducing a surrogate Gaussian process regression method, which significantly improves confidence interval accuracy across various base models and outperforms existing methods in medium-data regimes.
Machine learning models play a vital role in time series forecasting. These models, however, often overlook an important element: point uncertainty estimates. Incorporating these estimates is crucial for effective risk management, informed model selection, and decision-making.To address this issue, our research introduces a method for uncertainty estimation. We employ a surrogate Gaussian process regression model. It enhances any base regression model with reasonable uncertainty estimates. This approach stands out for its computational efficiency. It only necessitates training one supplementary surrogate and avoids any data-specific assumptions. Furthermore, this method for work requires only the presence of the base model as a black box and its respective training data. The effectiveness of our approach is supported by experimental results. Using various time-series forecasting data, we found that our surrogate model-based technique delivers significantly more accurate confidence intervals. These techniques outperform both bootstrap-based and built-in methods in a medium-data regime. This superiority holds across a range of base model types, including a linear regression, ARIMA, gradient boosting and a neural network.