Use Generative Models to Confirm Data Stationarity

Contributed by: Sofien Kaabar

Difficulty level: Easy

Stationarity is a fundamental concept in time series analysis, and it refers to a property of time series data where statistical properties, such as mean, variance, and autocorrelation, remain constant over time. In simpler terms, a stationary time series is one where the data points are not dependent on the time at which they are observed.

A stationary time series typically exhibits the following properties:

Constant mean

The mean of the time series remains constant over time, meaning the data points do not show a long-term trend (there are some exceptions that are out of scope of this Shortcut).

Constant variance

The variance (or standard deviation) of the data points remains constant over time, indicating that the spread of data points does not change systematically.

Constant autocorrelation

The autocorrelation function (ACF) of the time series remains constant for all time lags. This means that the relationship between data points at different time intervals does not change as time progresses.

Financial time series data often require stationarity for statistical modeling, forecasting, and other numerical techniques. Stationary time series are easy to model, because their properties remain constant over time. This makes it straightforward to apply traditional statistical methods and assumptions such as linear regression.

Techniques like differencing (taking price differences) can ...