In forecasting, the term seasonality doesn’t always mean what you think it does. It encompasses more than just patterns repeating from one season to the next. In fact, seasonality covers a wide range of periodic behaviors, and can have some issues associated with the naming conventions. Should we discuss?

First things first: when we say “seasonality” in forecasting, we mean any pattern that repeats periodically. If you mention monthly seasonality, most people will understand that you’re referring to a pattern repeating every 12 observations. Similarly, quarterly seasonality is widely recognized. However, beyond these two simple cases, ambiguity creeps in.

For example, if you describe your data as having “weekly” seasonality, do you mean that you’re working with weekly data and observe similar patterns every 52 weeks? Or are you dealing with daily data, where the pattern repeats every 7 days? The same issue applies to the term “daily” seasonality, which can refer to a pattern within daily data or a repeating pattern across multiple days.

Furthermore, the more granular your data, the more potential seasonal profiles you can have. For daily data, you may observe seasonality at 7-day (weekly) and 365-day (yearly) intervals. For hourly data, you could have three seasonal patterns: 24 hours, 168 hours (24 × 7), and 8,760 hours (24 × 365). An example of such data is shown in the image attached to this post.

Some people use the prefix “intra” to indicate patterns within a given frequency, but I still find this confusing. For example, intraweekly only indicates that a pattern exists within the week but doesn’t specify the frequency: it could refer to either 7 days or 168 hours.

That’s why I personally prefer the “A of B” naming scheme for seasonality. For example, “week of year” seasonality clearly denotes a pattern repeating every 52 observations. “Day of week” clearly refers to a 7-observation pattern. This format is more precise and less ambiguous than “weekly” or “intraweekly” seasonality. “Hour of year”, “half-hour of week”, “minute of day” etc are all straightforward and easy to understand.

And what naming conventions do you use?

P.S. Kandrika Pritularga and I are running the course “Demand Forecasting Principles with Examples in R” again, where we’ll discuss some of these and related aspects in detail. You can read more about the course and sign up for it here and here respectively.