Introduction to Time Series
In Chapter 9, Discriminant Analysis, we concluded our overview of statistical classification modeling by introducing conditional probability using Bayes’ theorem, Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). In this chapter, we will introduce time series, the underlying statistical concepts, and how to apply them in everyday analysis. We will introduce the topic with the distinction between time-series data and what we have discussed up to this point in the book. We then provide an overview of what to expect with time-series modeling and the goals it can be leveraged to achieve. Within the context of time series, we then reintroduce the mean and variance statistical parameters, in addition to correlation. We provide an overview of linear differencing, cross-correlation, and autoregressive (AR) and moving average (MA) properties and how to identify their ordering using autocorrelation function (ACF) and partial ACF...