Hands-On Machine Learning for Cybersecurity

Stochastic time series model

Stochastic processes are random mathematical objects that can be defined using random variables. These data points are known to randomly change over time. Stochastic processes can again be divided into three main classes that are dependent on historic data points. They are autoregressive (AR) models, the moving average (MA) model, and integrated (I) models. These models combine to form the autoregressive moving average (ARMA), the autoregressive integrated moving average (ARIMA), and the autoregressive fractional integrated moving average (ARFIMA). We will use these in later sections of the chapter.