Integrated Time Series

Integrated Time Series refers to a method in statistics where raw data points collected over time are transformed to analyze trends and patterns more effectively. This involves calculating cumulative sums or differences to make the data stationary—meaning its properties don’t change over time. By doing so, it becomes easier to identify underlying factors, forecast future values, and understand long-term behaviors in areas such as finance, economics, or environmental studies. Essentially, it helps researchers and analysts interpret complex time-based data by stabilizing it, allowing for more accurate and meaningful analysis of temporal patterns.