This paper proposes an On-line Weighted Ensemble (OWE) of regressor models which is able to learn incrementally sample by sample in the presence of several ...

Experiments with artificial and industrial data reveal that in most cases OWE outperforms other state-of-the-art concept drift approaches. Index Terms- Concept ...

An on-line weighted ensemble of regressor models to handle concept drifts ... model predictive control for batch trajectory tracking under concept drift.

Feb 20, 2020 · Bibliographic details on An on-line weighted ensemble of regressor models to handle concept drifts.

Jul 9, 2024 · In this paper, we propose Online Sequential FDSN (OSFDSN), which is similar to FDSN, but each of its SLFN modules has a weighted contribution to ...

This work investigates how to handle concept drift in financial time series prediction in order to improve the forecasting accuracy and proposes an approach ...

Concept drift refers to changes in the distribution of underlying data and is an inherent property of evolving data streams. Ensemble learning, with dynamic ...

Araújo, An on-line weighted ensemble of regressor models to handle concept drifts, Engineering Applications of Artificial Intelligence, 37 (2015) 392-406.

Nov 9, 2019 · The Additive Expert Ensemble (AddExp) was developed to deal with online classification tasks with concept drift (Kolter and Maloof 2005).

We compute the exponential weighted moving average (EWMA) of the last 6 predictions errors of both models respectively and determine the sum of errors.