Uncertainty estimation for time series forecasting via Gaussian process regression surrogates

• URL: http://arxiv.org/abs/2302.02834v1
• Date: Mon, 6 Feb 2023 14:52:56 GMT
• Title: Uncertainty estimation for time series forecasting via Gaussian process regression surrogates
• Authors: Leonid Erlygin, Vladimir Zholobov, Valeriia Baklanova, Evgeny Sokolovskiy, Alexey Zaytsev
• Abstract summary: We propose a new method for uncertainty estimation based on the surrogate Gaussian process model. Our method can equip any base model with an accurate uncertainty estimate produced by a separate surrogate. Compared to other approaches, the estimate remains computationally effective with training only one additional model.
• Score: 0.8733767481819791
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Machine learning models are widely used to solve real-world problems in science and industry. To build robust models, we should quantify the uncertainty of the model's predictions on new data. This study proposes a new method for uncertainty estimation based on the surrogate Gaussian process model. Our method can equip any base model with an accurate uncertainty estimate produced by a separate surrogate. Compared to other approaches, the estimate remains computationally effective with training only one additional model and doesn't rely on data-specific assumptions. The only requirement is the availability of the base model as a black box, which is typical. Experiments for challenging time-series forecasting data show that surrogate model-based methods provide more accurate confidence intervals than bootstrap-based methods in both medium and small-data regimes and different families of base models, including linear regression, ARIMA, and gradient boosting.

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