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

arXiv: https://arxiv.org/abs/2107.02463 · HTML: https://mlsanity.com/paper/2107.02463/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection](https://mlsanity.com/paper/1805.05383/) (2018)
- [Gaussian Processes for Nonlinear Signal Processing](https://mlsanity.com/paper/1303.2823/) (2013)
- [Sequential online prediction in the presence of outliers and change points: an instant temporal structure learning approach](https://mlsanity.com/paper/1907.06377/) (2019)

## Built on by (most cited first, 1 of 1)

- [FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting](https://mlsanity.com/paper/2604.22328/) (2026)

## Abstract (arXiv)

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.