CyberPhysical systems (CPS) must be closely monitored to identify and potentially mitigate emergent problems that arise during their routine operations. However, the multivariate time series data which they typically produce can be complex to understand and analyze. While formal product documentation often provides example data plots with diagnostic suggestions, the sheer diversity of attributes, critical thresholds, and data interactions can be overwhelming to non-experts who subsequently seek help from discussion forums. Deep learning models, such as Long Short-term memory (LSTM) networks can be used to automate these tasks and to provide clear explanations of diverse anomalies detected in real-time multivariate data-streams. In this paper we present RESAM, a systematic requirements process that integrates knowledge from domain experts, discussion forums, and formal product documentation, to discover and specify requirements for constructing effective deep learning anomaly detectors. We present a case-study based on a flight control system for Unmanned Aerial Vehicles and demonstrate that its use guides the construction of effective anomaly detection models whilst also providing underlying support for explainability.