EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data
This work addresses forecasting challenges for seasonal data with scale changes, offering an incremental improvement in efficiency and accuracy for time series applications.
The paper tackles the problem of forecasting seasonal time series with sudden shifts in scale by introducing EVARS-GPR, an online algorithm that combines change point detection and data augmentation for refitting, resulting in a 20.8% lower RMSE on real-world datasets and a six-fold runtime reduction compared to methods with similar computational costs.
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.