The use of machine learning for time series prediction has become increasingly popular across various industries thanks to the availability of time series data and advancements in machine learning algorithms. However, traditional methods for time series forecasting rely on pre-optimized models that are ill-equipped to handle unpredictable patterns in data. In this paper, we present EAMDrift, a novel method that combines forecasts from multiple individual predictors by weighting each prediction according to a performance metric. EAMDrift is designed to automatically adapt to out-of-distribution patterns in data and identify the most appropriate models to use at each moment through interpretable mechanisms, which include an automatic retraining process. Specifically, we encode different concepts with different models, each functioning as an observer of specific behaviors. The activation of the overall model then identifies which subset of the concept observers is identifying concepts in the data. This activation is interpretable and based on learned rules, allowing to study of input variables relations. Our study on real-world datasets shows that EAMDrift outperforms individual baseline models by 20% and achieves comparable accuracy results to non-interpretable ensemble models. These findings demonstrate the efficacy of EAMDrift for time-series prediction and highlight the importance of interpretability in machine learning models.

翻译：机器学习在时间序列预测中的应用因时间序列数据的可获取性及机器学习算法的进步，在众多行业中日益普及。然而，传统时间序列预测方法依赖预优化模型，难以应对数据中的不可预测模式。本文提出EAMDrift——一种通过依据性能指标对每个预测结果进行加权，从而整合多个独立预测器预测结果的新方法。EAMDrift旨在自动适应数据中的分布外模式，并通过可解释机制（包括自动再训练过程）实时识别最适用的模型。具体而言，我们以不同模型编码不同概念，每个模型作为特定行为的观察者。整体模型的激活过程可识别概念观察者中哪些子集正在识别数据中的概念。该激活过程基于学习规则具有可解释性，便于研究输入变量间的关系。在真实数据集上的实验表明，EAMDrift相比单个基线模型性能提升20%，且达到了与不可解释集成模型相当的预测精度。这些结果验证了EAMDrift在时间序列预测中的有效性，并强调了机器学习模型中可解释性的重要性。