Remember that time series analysis assumes that all the information needed to generate a forecast is contained in the time series of the data. The forecaster looks for patterns in the data and tries to obtain a forecast by projecting that pattern into the future. The easiest way to identify patterns is to plot the data and examine the resulting graphs. If we did that, what could we observe? There are four basic patterns, which are shown in Figure 8-1. Any of these patterns, or a combination of them, can be present in a time series of data:
Level or horizontal pattern
Pattern in which data values fluctuate around a constant mean.
Pattern in which data exhibit increasing or decreasing values over time.