With the advent of the Legacy Survey of Space and Time, time-domain astronomy will be faced with an unprecedented volume and rate of data. Real-time processing of variables and transients detected by such large-scale surveys is critical to identifying the more unusual events and allocating scarce follow-up resources efficiently. We develop an algorithm to identify these novel events within a given population of variable sources. We determine the distributions of magnitude changes (dm) over time intervals (dt) for a given passband f, p_f_^(dm|dt)^, and use these distributions to compute the likelihood of a test source being consistent with the population or being an outlier. We demonstrate our algorithm by applying it to the DECam multiband time-series data of more than 2000 variable stars identified by Saha+ (2019, J/ApJ/874/30) in the Galactic Bulge that are largely dominated by long-period variables and pulsating stars. Our algorithm discovers 18 outlier sources in the sample, including a microlensing event, a dwarf nova, and two chromospherically active RS CVn stars, as well as sources in the blue horizontal branch region of the color-magnitude diagram without any known counterparts. We compare the performance of our algorithm for novelty detection with the multivariate Kernel Density Estimator and Isolation Forest on the simulated PLAsTiCC data set. We find that our algorithm yields comparable results despite its simplicity. Our method provides an efficient way for flagging the most unusual events in a real-time alert-broker system.