  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.
