This chapter examines the topic of time series analysis and its applications. Emphasis is placed on identifying the underlying structure of the time series and fitting an appropriate Autoregressive Integrated Moving Average (ARIMA) model.
Time series analysis attempts to model the underlying structure of observations taken over time. A time series, denoted Y= a+bX, is an ordered sequence of equally spaced values over time. For example, Figure 8-1 provides a plot of the monthly number of international airline passengers over a 12-year period.
In this example, the time series consists of an ordered sequence of 144 values. The analyses presented in this chapter are limited to equally spaced time series of one variable. Following are the goals of time series analysis:
Time series analysis has many applications in finance, economics, biology, engineering, retail, and manufacturing. Here are a few specific use cases: