Sales prediction is crucial for business intelligence, aiding in workforce management or resource allocation. Accurate sales forecasting is vital for financial planning and predicting both short-term and long-term company performance. In this work, we propose the use of adaptive ensembles of classification models to accommodate different trends within the data, unlike typically used machine learning models. Our approach is based on a Mixture of Experts (MoE) model using LSTM networks, with block cross-validation. We compare our proposal to various standard models in prediction tasks. Experiments show that our model achieves greater generalization on unseen stores compared to other models. As future work, we plan to extend this model to Transformer models.