Some of the terms that we used in this chapter are:

• ARIMA: The ARIMA model was analyzed by looking at various aspects of the model. We gained an understanding of the auto-regressive and moving average component of ARIMA. We also looked at the p, d, and q elements of the model. We developed an understanding of how the process helps to deal with autocorrelation, in comparison to regression. We forecasted values from the model using the historical data from the variable of interest only.
• Dependent: The variable that we are trying to forecast or gain a better understanding of is the dependent. We can use a series of independent variables to try and forecast a dependent variable.
• Differencing: This is the transformation of the data that we have used to derive a new variable, based on the change of the series from one data point to another.
• Independent: The variables that help predict the business problem. The variation of these variables does not depend on other variables.
• Multicollinearity: Multicollinearity is said to occur when two or more similar, independent variables are highly correlated with the dependent variable.
• Regression: This is the statistical measure/model that evaluates the relationship between a dependent and a series of independent variables. We reviewed three different types of outputs from regression models. These are the fit statistics, diagnostic plots, and residual plots.
• Seasonality: A phenomenon that we see occurring over an at least one-year period, where the change is predictable.
• Stationarity: A stationarity process in one in which the mean, variance, and autocorrelation are constant across time. A non-stationarity process has many disadvantages, as we reviewed.
• Trend: This shows a pattern within the data. This could be simply a linear pattern on increasing the stock price, when plotted against the time series.