What is one characteristic of a stationary time series?

A stationary time series is characterized by statistical properties that do not change over time. This means that its mean, variance, and autocovariance remain constant, making it consistent in terms of its value distribution and fluctuations.

The characteristic of remaining consistent over time means that the patterns observed within the data, such as trends or seasonality, do not change. This property is crucial for time series analysis, as many statistical methods, like ARIMA modeling, assume a stationary process to make reliable forecasts.

In contrast, volatile fluctuations indicate irregular changes in the data, which are not a feature of a stationary series. Relying solely on qualitative data does not pertain to the nature of the data series itself but rather to the type of data used for analysis. Forecasting using multiple variables refers to multivariate analysis, which is distinct from the univariate nature of a stationary time series. Therefore, the option highlighting consistency aligns perfectly with the definition and properties of a stationary time series.