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.

*Level or horizontal*. A**level or horizontal pattern**exists when data values fluctuate around a constant mean. This is the simplest pattern and the easiest to predict. An example is sales of a product that do not increase or decrease over time. This type of pattern is common for products in the mature stage of their life cycle, in which demand is steady and predictable.**Trend**Pattern in which data exhibit increasing or decreasing values over time.

*Trend*. When data exhibit an increasing or decreasing pattern over time, we say that they exhibit a**trend**. The trend can be upward or downward. The simplest type of trend is a straight line, or ...

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