  In this paper, we use a variance-based genetic ensemble (VGE) of Neural
Networks (NNs) to detect anomalies in the satellite's historical data. We use
an efficient ensemble of the predictions from multiple Recurrent Neural
Networks (RNNs) by leveraging each model's uncertainty level (variance). For
prediction, each RNN is guided by a Genetic Algorithm (GA) which constructs the
optimal structure for each RNN model. However, finding the model uncertainty
level is challenging in many cases. Although the Bayesian NNs (BNNs)-based
methods are popular for providing the confidence bound of the models, they
cannot be employed in complex NN structures as they are computationally
intractable. This paper uses the Monte Carlo (MC) dropout as an approximation
version of BNNs. Then these uncertainty levels and each predictive model
suggested by GA are used to generate a new model, which is then used for
forecasting the TS and AD. Simulation results show that the forecasting and AD
capability of the ensemble model outperforms existing approaches.
