How to remove non-stationarity?

Trend:

• Detrending: Remove the trend component from the data. This can be achieved by fitting a regression line or using techniques like moving averages.
• Differencing: Take the difference between consecutive observations to remove the trend. This can be done once or multiple times until the data becomes stationary.

Seasonality:

• Seasonal Adjustment: Use techniques such as seasonal decomposition of time series (e.g., STL decomposition) to separate the seasonal component from the data.
• Seasonal Differencing: Take differences between observations at the same season of different years to remove seasonality.

Variance:

• Transformation: Apply transformations such as logarithmic, square root, or Box-Cox transformation to stabilize the variance and make it more constant over time.

Autocorrelation:

• Differencing: Besides removing trends, differencing can also help reduce autocorrelation by eliminating dependence between consecutive observations.
• Autoregressive Integrated Moving Average (ARIMA): Utilize ARIMA models, which incorporate differencing to handle autocorrelation.

Removing non-stationarity in time series data is crucial for accurate forecasting because many time series forecasting models assume stationarity, where the statistical properties of the time series do not change over time. Non-stationarity can manifest as trends, seasonality, or other forms of irregular patterns in the data.

The article comprehensively covers techniques and tests for removing non-stationarity in time series data, crucial for accurate forecasting, including detrending, seasonal adjustment, logarithmic transformation, differencing, and ADF/KPSS tests for stationarity validation.