  Recently, there has been a significant amount of interest in satellite
telemetry anomaly detection (AD) using neural networks (NN). For AD purposes,
the current approaches focus on either forecasting or reconstruction of the
time series, and they cannot measure the level of reliability or the
probability of correct detection. Although the Bayesian neural network
(BNN)-based approaches are well known for time series uncertainty estimation,
they are computationally intractable. In this paper, we present a tractable
approximation for BNN based on the Monte Carlo (MC) dropout method for
capturing the uncertainty in the satellite telemetry time series, without
sacrificing accuracy. For time series forecasting, we employ an NN, which
consists of several Long Short-Term Memory (LSTM) layers followed by various
dense layers. We employ the MC dropout inside each LSTM layer and before the
dense layers for uncertainty estimation. With the proposed uncertainty region
and by utilizing a post-processing filter, we can effectively capture the
anomaly points. Numerical results show that our proposed time series AD
approach outperforms the existing methods from both prediction accuracy and AD
perspectives.
