Non-stationary Processes

Non-stationary processes are patterns or systems whose statistical properties, such as mean and variance, change over time. Unlike stationary processes, which remain consistent, non-stationary ones can exhibit trends, cycles, or increasing variability, making their behavior unpredictable in the long run. Examples include stock prices, weather patterns, or economic indicators that evolve over time. Understanding non-stationarity is important because it affects how we model, analyze, and forecast such processes, requiring methods that account for their changing nature to make accurate predictions or inferences.