Stationarity Testing (Augmented Dickey-Fuller Test) A stationarity test helps determine whether a time series is stationary, meaning its statistical propert...
Stationarity Testing (Augmented Dickey-Fuller Test)
A stationarity test helps determine whether a time series is stationary, meaning its statistical properties remain constant over time. A stationary time series exhibits the following characteristics:
First, the mean of the series is constant.
Second, the autocorrelation function (ACF) is zero for all lags except the first one.
Third, the PACF (partial autocorrelation function) is zero for all lags except the first and last ones.
If these conditions are met, the time series is considered stationary and can be used for forecasting. However, if the series shows any signs of non-stationarity, it might not be suitable for forecasting.
Example:
Imagine a time series of daily closing prices of a stock. The mean of the series might be constant, but the autocorrelation between prices at different lags could show a significant positive correlation. This would suggest non-stationarity, rendering the time series unsuitable for forecasting.
Key Points:
A stationary time series has constant statistical properties.
The ACF and PACF help identify non-stationarity by examining the relationship between lags.
Non-stationary time series cannot be used for accurate forecasting