Hands-On Machine Learning for Cybersecurity

ARFIMA

This is a generalized ARIMA model that allows non-integer values of the differencing parameter. It is in time series models with a long memory.

Now that we have discussed the details of each of these stochastic models, we will fit the model with the baseline network data. We will use the stats models library for the ARIMA stochastic model. We will pass the p, d, q values for the ARIMA model. The lag value for autoregression is set to 10, the difference in order is set to 1, and the moving average is set to 0. We use the fit function to fit the training/baseline data. The fitting model is shown as follows:

# fitting the modelmodel = ARIMA(new_count_df, order=(10,1,0))fitted_model = model.fit(disp=0)print(fitted_model.summary())

The ...