Time Series > Seasonality
What is Seasonality?
Seasonality refers to periodic fluctuations in time series data that happens at regular periods. While traditionally used to literally mean seasons (e.g. Spring, Summer, Autumn, Winter), it can occur during any time period, like hours, days, or weeks.
- Sales data tends to increase before the December holidays and then decreases into the new year.
- Monthly temperatures in any city tend to rise and fall predictably from year to year.
- Hourly sales data for “big box” stores open 24 hours will rise and fall predictably at certain times of the day, with peaks at dinnertime/after work and lows at 3-4 a.m..
Seasonality can cause issues with interpreting time series data and so must be included in any model. While seasonal variations–changes that occur in a particular season of the year–are fairly easy to detect in data (a simple scatter plot can often show the trends), seasonality is harder to detect because you don’t know what time periods are fluctuating. Various techniques are available to detect these fluctuations including:
- A run sequence plot or multiple box and whiskers charts. Easy to read, but assumes you know the seasonal periods.
- A seasonal subseries plot. Assumes you already know the seasonal periods. Good for small data sets; Plots for larger data sets can be hard to read.
- A correlogram (or autocorrelation function plot). Useful if you don’t know the seasonal period. Seasonal periods usually show up as spiked at seasonal intervals.
The above image of two subseries plots demonstrates the obvious seasonal trend in the right-hand plot: the data decreases to March, increases towards August and then decreases again. the plot on the left shows no obvious pattern. If you plotted a series of box plots (one for each month), the results would be similar.
Other Factors that Cause Fluctuations
Seasonality is just one component that can cause fluctuations in time series data. For example, graphs can have trend components (an overall increase or decrease), cyclical components (wave like patterns) and irregular components (unpredictable, random fluctuations). Cyclical components are very similar to seasonality. However, while seasonality follows a regular pattern (e.g. monthly or quarterly), the time intervals between cyclical components vary.Comments are now closed for this post. Need help or want to post a correction? Please post a comment on our Facebook page and I'll do my best to help!