  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.
