Augmented Dickey-Fuller Test

The Augmented Dickey-Fuller (ADF) test is a statistical method used to check if a time series, like stock prices or temperature data, is stable over time or if it has a pattern of changing (trending or drifting). It helps determine whether the data is "stationary" (consistent in its behavior) or "non-stationary" (changing over time). This is important in forecasting and modeling because many models assume data is stationary. The test does this by analyzing the data's patterns and providing a numerical result: if the data is stationary, it suggests the process doesn't have a trend or long-term change.