A. Handling non-stationary time series typically involves transforming the data to make it stationary or modeling the non-stationarity explicitly. Common techniques include: Detrending: Removing trend components from the data to make it stationary. This can involve techniques like linear regression or differencing. Differencing: Taking differences between consecutive observations to remove trends or seasonal patterns. Transformation: Applying mathematical transformations such as logarithms or square roots to stabilize the variance. Decomposition: Decomposing the time series into its trend, seasonal, and residual components and modeling each separately. Modeling: Using models that explicitly account for non-stationarity, such as autoregressive integrated moving average (ARIMA) models or seasonal decomposition of time series (STL).