EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data

• URL: http://arxiv.org/abs/2107.02463v1
• Date: Tue, 6 Jul 2021 08:20:28 GMT
• Title: EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data
• Authors: Florian Haselbeck and Dominik G. Grimm
• Abstract summary: We present EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data (EVARS-GPR) Our algorithm is able to handle sudden shifts in the target variable scale of seasonal data. EVARS-GPR has on average a 20.8 % lower RMSE on different real-world datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series forecasting is a growing domain with diverse applications. However, changes of the system behavior over time due to internal or external influences are challenging. Therefore, predictions of a previously learned fore-casting model might not be useful anymore. In this paper, we present EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data (EVARS-GPR), a novel online algorithm that is able to handle sudden shifts in the target variable scale of seasonal data. For this purpose, EVARS-GPR com-bines online change point detection with a refitting of the prediction model using data augmentation for samples prior to a change point. Our experiments on sim-ulated data show that EVARS-GPR is applicable for a wide range of output scale changes. EVARS-GPR has on average a 20.8 % lower RMSE on different real-world datasets compared to methods with a similar computational resource con-sumption. Furthermore, we show that our algorithm leads to a six-fold reduction of the averaged runtime in relation to all comparison partners with a periodical refitting strategy. In summary, we present a computationally efficient online fore-casting algorithm for seasonal time series with changes of the target variable scale and demonstrate its functionality on simulated as well as real-world data. All code is publicly available on GitHub: https://github.com/grimmlab/evars-gpr.

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