Models for stationary time series
In this section, we will discuss Autoregressive (AR), Moving Average (MA), and Autoregressive Moving Average (ARMA) models that are useful for stationary data. These models are useful when modeling patterns and variance around process means that output over time. When we have data that does not exhibit autocorrelation, we can use statistical and machine learning models that do not make assumptions about time, such as Logistic Regression or Naïve Bayes, so long as the data supports such use cases.
Autoregressive (AR) models
The AR(p) model
In Chapter 10, Introduction to Time Series we considered how the Partial Auto-Correlation Function (PACF) correlates one data point to another lag, controlling for those lags between. We also discussed how inspection of the PACF plot is a frequently used method for assessing the ordering of an autoregressive model. Thereto, the autoregressive model is one that considers specific points in the past to...
