## 1 Introduction  
In telecommunications, predicting customer churn--when customers stop using a service--is pivotal for maintaining profitability and market share [1]. Effective churn prediction models enable companies to implement targeted retention strategies, reducing the costs associated with acquiring new customers [2]. However, the application of machine learning to telecommunications data is prone to concept drift effects--where the relationships between input features and the target variable change over time [3]. This drift can lead to qualitative differences between the data used for training the model and the data encountered in its real-world application, resulting in a degraded prediction performance [4]. Often, concept drift is investigated as a stand-alone problem using change-detection methods; for example, by monitoring distributions over two different time windows using a wide range of techniques to identify drift [5]. However, in this article we investigate the effect of concept drift in the most straightforward way--we compare the performance of the same model when applied to different time windows. This means that we do not address the problem of concept drift itself but are able to measure it precisely in the context of our used data and models. Traditional machine learning methodologies typically divide a dataset into training, validation, and testing subsets [6]. Training data are used to fit a model; validation is used to supervise the model's convergence and prevent overfitting; test data are used for the final performance evaluation. While effective in controlled settings, this approach might fail to take into account the evolving nature of customer behavior and market conditions [7]. Consequently, models trained on historical data may underperform when applied to future data, establishing the need to address concept drift [3]. Thus, the authors of some recent research directly considered the effects of concept drift when developing their machine learning methods [8]. Several studies have attempted to enhance churn prediction models using various techniques. Deep learning models, such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), have been employed for their ability to capture complex temporal patterns in customer behavior [9; 10]. [PERSON] and [PERSON] [9] developed a churn prediction model using a Swish RNN with a novel feature selection strategy, achieving high accuracy. However, deep learning models often require large datasets and significant computational resources, which may not be practical for all organizations [9]. Furthermore, deep learning frameworks can be sensitive to imbalanced datasets, a frequent challenge in churn scenarios, which often contain far fewer churners than non-churners. Class imbalances, a common issue in churn prediction due to the low proportion of churners, have been addressed using techniques like SMOTE combined with optimization algorithms [11; 12]. [PERSON] et al. [11] combined SMOTE with an Optimal Weighted Extreme Learning Machine to improve predictive performance. Similarly, [PERSON] et al. [12] proposed a hybrid model that uses SMOTE and Particle Swarm Optimization for feature selection. However, even when using balanced training data, model stability can still be improved by combining different learning algorithms, i.e., using of ensemble methods. Ensemble learning methods have also been employed to enhance model robustness and accuracy [13; 14; 15]. [PERSON] and [PERSON] [14] benchmarked ensemble methods, finding that heterogeneous ensembles generally outperformed other models. [PERSON] and [PERSON] [13] developed a hybrid approach using machine learning algorithms for churn prediction in the telecommunications industry. [PERSON] et al. [15] employed clustering to establish groups of customers before applying the ensemble learning techniques, recognizing that the factors that influence churn may vary among different consumer groups.