Description: Non-stationary processes are those in which statistical properties, such as mean and variance, change over time. This characteristic distinguishes them from stationary processes, where these properties remain constant. In the context of hyperparameter and model optimization, non-stationary processes present a significant challenge, as the conditions under which models are trained and evaluated can vary, affecting their performance and generalization. For example, in machine learning, a model that fits well to a dataset at a given moment may not be effective if the data characteristics change over time. This can occur in various applications, such as time series analysis and pattern recognition, where patterns may evolve due to external factors. Identifying and adapting to these changes is crucial for maintaining model effectiveness. Therefore, non-stationary processes require optimization techniques that are flexible and capable of adapting to new conditions, often involving the use of online or adaptive learning methods. In summary, the dynamic nature of non-stationary processes complicates the optimization task, demanding a more sophisticated and time-aware approach in the development of predictive models.
