In this paper, we propose an unsupervised data-driven approach to predict real-time locational marginal prices (RTLMPs). The proposed approach is built upon a general data structure for organizing system-wide heterogeneous market data streams into the format of market data images and videos. Leveraging this general data structure, the system-wide RTLMP prediction problem is formulated as a video prediction problem. A video prediction model based on generative adversarial networks (GAN) is proposed to learn the spatio-temporal correlations among historical RTLMPs and predict system-wide RTLMPs for the next hour. An autoregressive moving average (ARMA) calibration method is adopted to improve the prediction accuracy. The proposed RTLMP prediction method takes public market data as inputs, without requiring any confidential information on system topology, model parameters, or market operating details. Case studies using public market data from ISO New England (ISO-NE) and Southwest Power Pool (SPP) demonstrate that the proposed method is able to learn spatio-temporal correlations among RTLMPs and perform accurate RTLMP prediction.