How to Model Multiple Seasonality in Time Series

Handling seasonal effects in several periods

Photo by Joshua Woroniecki on Unsplash

In this article, you’ll learn how to model multiple seasonality in time series. We’ll cover:

• How to decompose a time series using MSTL
• Creating explanatory variables that capture complex seasonality
• Using off-the-shelf methods, with an example based on orbit’s forecasting package.

Complex Seasonality

Seasonality refers to systematic changes that repeat with a regular periodicity. These patterns are connected with the frequency at which a time series is observed. A low-frequency time series usually contains a single seasonal period. For example, monthly time series exhibit yearly seasonality.

Increasingly, time series are collected at higher sampling frequencies, such as daily or hourly. This leads to larger datasets with a complex seasonality. A daily time series may show weekly, monthly, and yearly repeating patterns.

Here’s an example of an hourly time series with daily and weekly seasonality: