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Similar to how multivariate analysis is the analysis of relationships between multiple variables, univariate analysis is the quantitative analysis of only one variable at a time. When you model univariate time series, you’re modeling time series changes that represent changes in a single variable over time.

Autoregressive moving average (ARMA) is a class of forecasting methods that you can use to predict future values from current and historical data. As its name implies, the family of ARMA models combines autoregression techniques (analyses that assume that previous observations are good predictors of future values and perform an autoregression analysis to forecast for those future values) and moving average techniques — models that measure the level of the constant time series and then update the forecast model if any changes are detected. If you’re looking for a simple model or a model that will work for only a small dataset, the ARMA model isn’t a good fit for your needs. An alternative in this case might be to just stick with simple linear regression. In Figure 4-10, you can see that the model forecast data and the actual data are a close fit.

To use the ARMA model for reliable results, you need to have at least 50 observations.

FIGURE 4-10 An example of an ARMA forecast model.