Visualizing time series: suppressing one pattern to enhance another pattern

Visualizing a time series is an essential step in exploring its behavior. Statisticians think of a time series as a combination of four components: trend, seasonality, level and noise. All real-world series contain a level and noise, but not necessarily a trend and/or seasonality. It is important to determine whether trend and/or seasonality exist in a series in order to choose appropriate models and methods for descriptive or forecasting purposes. Hence, looking at a time plot,  typical questions include:

• is there a trend? if so, what type of function can approximate it? (linear, exponential, etc.) is the trend fixed throughout the period or does it change over time? 
• is there seasonal behavior? if so, is seasonality additive or multiplicative? does seasonal behavior change over time?

Exploring such questions using time plots (line plots of the series over time) is enhanced by suppressing one type of pattern for better visualizing other patterns. For example, suppressing seasonality can make a trend more visible. Similarly, suppressing a trend can help see seasonal behavior. How do we suppress seasonality? Suppose that we have monthly data and there is apparent annual seasonality. To suppress seasonality (also called seasonal adjustment), we can

1. Plot annual data (either annual averages or sums)
2. Plot a moving average (an average over a window of 12 months centered around each particular month)
3. Plot 12 separate series, one for each month (e.g., one series for January, another for February and so on)
4. Fit a model that captures monthly seasonality (e.g., a regression model with 11 monthly dummies) and look at the residual series

An example is shown in the Figure. The top left plot is the original series (showing monthly ridership on Amtrak trains). The bottom left panel shown a moving average line, suppressing seasonality and showing the trend. The top right panel shows a model that captures the seasonality. The lower left panel shows the residuals from the model, again enhancing the trend.

For further details and examples, see my recently published book Practical Time Series Forecasting: A Hands On Guide (available in soft-cover and as an eBook).