EVARS-GPR: EVent-Triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data
Abstract Timeseries 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 forecast- ing 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 combines online change point detection with a refitting of the prediction model using data aug- mentation for samples prior to a change point. Our experiments on simulated 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 consumption. Fur- thermore, 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 forecasting 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.
Haselbeck, Florian, and Dominik G. Grimm. “EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data.” German Conference on Artificial Intelligence (Künstliche Intelligenz). Springer, Cham, 2021. (https://doi.org/10.1007/978-3-030-87626-5_11)
Preprint available on ArXiv (no paywall): https://arxiv.org/abs/2107.02463