This example represents a simple, stationary time-series model, which is independent of previous values. Example 2: ARMA (2,1) model,python import numpy as np import statsmodels.api as sm # Generate a sequence of random data data = np.random.randn(100) # Fit an ARMA(2,1) model model = sm.tsa.ARMA(data, order=(2,1)) results = model.fit() # Print summary of the model print(results.summary()) ``` This example is similar to the above example, but it is modeled with the ARMA (2,1) model. Overall, Python statsmodels.tsa.arima_model ARMA is a powerful package library for modeling complex data. It is an important tool for data analysis and can be used for a variety of time-series data analysis applications.