Steady-state detection is of vital importance for experiments and simulations in chemical engineering, as well as also other fields of science, engineering, and financeparticularly when the full timescale of interest cannot be measured or simulated. We present a breakthrough for estimating the number of data points required before successful steady-state detection is feasible. Using an initial window of data, the method enables predicting the prerequisites for steady state detection (ppSSD), given as a number of data points. The method is shown to be accurate for data with realistic distributions (uniform, normal, and sine-wave), and data from actual kinetic Monte Carlo simulations. Users need only to use the algebraic equations derived and provided in this work to estimate the required number of data points for relevant steady-state detection. (c) 2018 American Institute of Chemical Engineers AIChE J, 64: 3354-3359, 2018