What is the purpose of differencing in time series analysis?

Differencing in time series analysis is primarily used to eliminate correlation among the data, particularly to remove trends and seasonality that can obscure the underlying patterns of the data. When the data has a correlation structure over time, such as an upward or downward trend, it can mask the true behavior of the data points. By applying differencing—a technique where the difference between successive observations is calculated—analysts can transform a non-stationary time series into a stationary one, which is essential for many statistical methods and forecasting models.

A stationary series has constant mean and variance over time and no autocorrelation, making it easier to model and predict future values. This process is crucial for identifying reliable patterns and understanding the long-term trends without the noise of correlation affecting the analysis. The other options do not accurately reflect the primary purpose of differencing.