# Handling Concept Drifts in Regression Problems -- the Error Intersection Approach

arXiv: https://arxiv.org/abs/2004.00438 · HTML: https://mlsanity.com/paper/2004.00438/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2020.

## Built on by (most cited first, 6 of 6)

- [The Forecast After the Forecast: A Post-Processing Shift in Time Series](https://mlsanity.com/paper/2601.20280/) (2026)
- [Handling Concept Drift in Global Time Series Forecasting](https://mlsanity.com/paper/2304.01512/) (2023)
- [OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams](https://mlsanity.com/paper/2308.15059/) (2023)
- [Explaining Inference Queries with Bayesian Optimization](https://mlsanity.com/paper/2102.05308/) (2021)
- [DeepBooTS: Dual-Stream Residual Boosting for Drift-Resilient Time-Series Forecasting](https://mlsanity.com/paper/2511.06893/) (2025)
- [Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Series](https://mlsanity.com/paper/2302.03302/) (2023)

## Abstract (arXiv)

Machine learning models are omnipresent for predictions on big data. One challenge of deployed models is the change of the data over time, a phenomenon called concept drift. If not handled correctly, a concept drift can lead to significant mispredictions. We explore a novel approach for concept drift handling, which depicts a strategy to switch between the application of simple and complex machine learning models for regression tasks. We assume that the approach plays out the individual strengths of each model, switching to the simpler model if a drift occurs and switching back to the complex model for typical situations. We instantiate the approach on a real-world data set of taxi demand in New York City, which is prone to multiple drifts, e.g. the weather phenomena of blizzards, resulting in a sudden decrease of taxi demand. We are able to show that our suggested approach outperforms all regarded baselines significantly.