Which of the following methods is used to ensure stationarity in time series data, a critical prerequisite for ARIMA modeling?

Differencing is a common technique used to ensure stationarity in time series data. A stationary time series has constant mean, variance, and autocovariance over time. Differencing involves subtracting the previous observation from the current one, which helps remove trends and seasonality from the data. This technique makes the data more stationary and thus suitable for time series modeling techniques like ARIMA, which assume that the underlying data is stationary. The first difference of a time series typically removes linear trends, and higher-order differencing can address more complex patterns. Why Other Options Are Incorrect: • A: Removing outliers is a data cleaning technique but does not directly address the stationarity of time series data. • B: Normalization adjusts the scale of the data but does not make the series stationary. • D: Transforming data into a different distribution may be useful for other purposes but is not primarily used to achieve stationarity. • E: Moving averages smooth the data but do not ensure stationarity, as they do not directly remove trends or seasonality.