Moving forward to our modern history, time-series chart is the most frequently used form to display data. According to Edward Tufte, time series made 75 percent of the charts in a random sample of 4,000 graphics published in leading international newspapers between 1974 and 1980—R. Edward Tufte, “The Visual Display of Quantitative Information”, 2001, Graphics Press.
To understand the popularity of the time-series we need to look no further than one of the manager's most important roles: forecaster. Indeed, the primary goal of the analysis of a time series is forecasting—Hence, its high frequency of appearance in business reports. But in order to forecast we first need to identify patterns in the observed data. Once patterns are identified, valuable insights into the movement of the data might be gained and forecasting into the future becomes an easier task.
Different patterns—trend, cyclicality, seasonality and irregularity—are of interest to the forecaster. But we're going to focus here on seasonality. By seasonality, we mean periodic fluctuations. For example, a coastal resort located on the Mediterranean experiences higher occupancies in the summer compared to a ski resort in the Alps where winter is the high season. So time series of hotel occupancies will typically show peaking of demand at different times of the year.
Luckily there are different graphical techniques that can be used to detect seasonality. To illustrate the techniques we're going to use the data shown in the table below. The numbers represent overall market hotel room occupancies of a hypothetical tourism destination from 2006 through 2013 and for twelve months of the year.