Description: The non-stationary process refers to a generative model that considers changes in the underlying data distribution over time. Unlike stationary processes, where statistical properties remain constant, non-stationary processes are dynamic and can exhibit significant variations in their characteristics. This implies that models based on these processes must be able to adapt to new conditions and emerging patterns. In the context of data science and statistics, analyzing non-stationary processes is crucial for prediction and decision-making, as it allows analysts and data scientists to understand how variables may change over time and how these changes can influence future outcomes. In the realm of machine learning frameworks, models are developed that can capture these temporal dynamics, using techniques such as recurrent neural networks (RNNs) and time series models. The ability to model non-stationary processes is essential in applications ranging from economics to meteorology, where conditions can vary drastically and unpredictably.
