What is the first step in applying an ARIMA model?

The first step in applying an ARIMA (AutoRegressive Integrated Moving Average) model is indeed to ensure that the data is stationary. Detrending prices is a crucial aspect of this process, as non-stationary data can lead to unreliable and biased forecasts.

Detrending involves removing trends and seasonality from the data, which may obscure the underlying patterns essential for effective time series analysis. By transforming the data to eliminate trends, practitioners can more clearly identify the autoregressive and moving average components that the ARIMA model relies on. This process typically involves differencing the data, which can help stabilize the mean of the time series by removing changes in the level of a series, thus facilitating better model fitting and forecasting.

Once the data is detrended, the next steps would typically involve checking for stationarity, estimating parameters, and finally fitting the model, so ensuring that prices are detrended lays the foundational work necessary for a valid application of the ARIMA methodology.