This document discusses ranking refactoring suggestions based on historical code volatility. It proposes using the historical volatility of code elements, measured by changes across prior versions, to prioritize refactoring efforts. It evaluates four forecasting models - random walk, historical average, exponential smoothing, and exponentially weighted moving average - for predicting future volatility to rank suggestions. The historical average model achieved the lowest error and produced rankings most similar to actual volatility, especially for elements with frequent changes, making it a suitable strategy. The goal is to focus refactoring on code most likely to undergo future maintenance changes.