Description: Volatility adjustment is a fundamental process in data preprocessing that involves modifying data to account for changes in volatility over time. This process is crucial in time series analysis, where fluctuations in data can significantly affect the outcomes of predictive models. Volatility refers to the variability or dispersion of data, and its adjustment allows analysts and data scientists to obtain a more accurate representation of underlying trends. By adjusting volatility, the goal is to stabilize the variance of the data, which facilitates the identification of patterns and the execution of more reliable forecasts. This process may include techniques such as normalization, standardization, or the use of statistical models that incorporate volatility, such as GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models. In summary, volatility adjustment is an essential tool for improving data quality and the effectiveness of analyses in contexts where variability is a critical factor.
