by Time series datasets can contain a seasonal component.
This is a cycle that repeats over time, such as monthly or yearly. This repeating cycle may obscure the signal that we wish to model when forecasting, and in turn may provide a strong signal to our predictive models.
- The definition of seasonality in time series and the opportunity it provides for forecasting with machine learning methods.
- How to use the difference method to create a seasonally adjusted time series of daily temperature data.
- How to model the seasonal component directly and explicitly subtract it from observations.
Seasonality in Time Series
Time series data may contain seasonal variation.
Seasonal variation, or seasonality, are cycles that repeat regularly over time.
A repeating pattern within each year is known as seasonal variation, although the term is applied more generally to repeating patterns within any fixed period.
Introductory Time Series with R
A cycle structure in a time series may or may not be seasonal. If it consistently repeats at the same frequency, it is seasonal, otherwise it is not seasonal and is called a cycle.
Benefits to Machine Learning
Understanding the seasonal component in time series can improve the performance of modeling with machine learning.
This can happen in two main ways:
- Clearer Signal: Identifying and removing the seasonal component from the time series can result in a clearer relationship between input and output variables.
- More Information: Additional information about the seasonal component of the time series can provide new information to improve model performance.
Both approaches may be useful on a project. Modeling seasonality and removing it from the time series may occur during data cleaning and preparation.
Extracting seasonal information and providing it as input features, either directly or in summary form, may occur during feature extraction and feature engineering activities.
Types of Seasonality
There are many types of seasonality; for example:
- Time of Day.
- Daily.
- Weekly.
- Monthly.
- Yearly.
As such, identifying whether there is a seasonality component in your time series problem is subjective.
The simplest approach to determining if there is an aspect of seasonality is to plot and review your data, perhaps at different scales and with the addition of trend lines.
Removing Seasonality
Once seasonality is identified, it can be modeled.
The model of seasonality can be removed from the time series. This process is called Seasonal Adjustment, or Deseasonalizing.
A time series where the seasonal component has been removed is called seasonal stationary. A time series with a clear seasonal component is referred to as non-stationary.
There are sophisticated methods to study and extract seasonality from time series in the field of Time Series Analysis. As we are primarily interested in predictive modeling and time series forecasting, we are limited to methods that can be developed on historical data and available when making predictions on new data.
In this tutorial, we will look at two methods for making seasonal adjustments on a classical meteorological-type problem of daily temperatures with a strong additive seasonal component. Next, let’s take a look at the dataset we will use in this tutorial.
