Guest blogger: Snurre Jensen on seasonality vs. cyclicity (part 2 of 2)

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Look at the following graphs showing one weekly time series. The left hand shows the actual time series plot.

To the less statistically inclined this plot might indicate that the data is seasonal due to the troughs during summer and the peaks during winter. However, if you were to use a seasonal statistical forecasting model you would probably get poor results.

The reason is shown in the right hand side of the panel. This graph shows the seasonal cycles in the time series. Since this is weekly data there are 52 values on the x-axis. Compare this graph to the graph shown in Part 1, and it is quite clear that the lines are far from parallel – and hence no visual indication of the presence of seasonality in the data.

One reason for the confusion could be that the concept of seasonality in statistical forecasting is dealing with cycles of fixed lengths whereas we all know hardly anything in the business world is of fixed length.

One could argue that when it comes to statistical forecasting we would be better off if seasonality was called something like “cyclicity” instead.  One reason why this is not the case could be that in early days of statistical forecasting the frequencies being considered were monthly and quarterly where seasonality and “cyclicity” coincide.

The challenge then becomes one of communication. How do you communicate that using seasonal forecasting models on seasonal data is probably not advisable?

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Mike Gilliland

Product Marketing Manager

Michael Gilliland is author of The Business Forecasting Deal (the book), and editor of Business Forecasting: Practical Problems and Solutions. He is a longtime business forecasting practitioner, and currently Product Marketing Manager for SAS Forecasting software. Mike serves on the Board of Directors for the International Institute of Forecasters, and received the 2017 Lifetime Achievement in Business Forecast award from the Institute of Business Forecasting. He initiated The Business Forecasting Deal (the blog) to help expose the seamy underbelly of forecasting practice, and to provide practical solutions to its most vexing problems.

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