Handling non-stationarity involves several approaches:
1. Differencing: This technique involves computing the differences between consecutive observations to remove trends. 2. Decomposition: Decomposing the time series into trend, seasonal, and residual components can help isolate and analyze each aspect separately. 3. Modeling with Non-Stationary Methods: Using models that account for non-stationarity, such as ARIMA (Auto-Regressive Integrated Moving Average) models, can provide more accurate predictions.
