Which of the following methods is most commonly used for ensuring that time series data is stationary?

Differencing is a technique that transforms non-stationary time series data into stationary data by subtracting the previous observation from the current observation. This process helps remove trends and seasonality, making the data suitable for time series models that assume stationarity, such as ARIMA. By achieving stationarity, differencing allows for consistent statistical properties across time, ensuring that mean, variance, and autocorrelation remain constant. This is particularly critical in time series forecasting, where reliable predictions depend on data stability, allowing analysts to model data accurately and account for historical patterns. The other options are incorrect because: • Option 1 (Exponential Smoothing) smooths data but does not specifically address stationarity. • Option 2 (Moving Average) is used for smoothing data but does not inherently stabilize mean and variance. • Option 4 (Seasonal Adjustment) removes seasonal effects but may not make data stationary. • Option 5 (Decomposition) breaks data into components but does not necessarily make it stationary.