How to Identify and Remove Seasonality from Time Series Data with Python

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.

In this tutorial, you will discover how to identify and correct for seasonality in time series data with python.

After completing this tutorial, you will know:

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.

Let’s get started.

How to Identify and Remove Seasonality from Time Series Data with Python

Photo by naturalflow , some rights reserved.

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.

― Page 6, 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.

Minimum Daily Temperatures Dataset

This dataset describes the minimum daily temperatures over 10 years (1981-1990) in the city Melbourne, Australia.

The units are in degrees Celsius and there are 3,650 observations. The source of the data is credited as the Australian Bureau of Meteorology.

Below is a sample of the first 5 rows of data, including the header row.

"Date","Temperature" "1981-01-01",20.7 "1981-01-02",17.9 "1981-01-03",18.8 "1981-01-04",14.6 "1981-01-05",15.8

Below is a plot of the entire dataset taken from Data Market where you can download the dataset and learn more about it.

Minimum Daily Temperatures

The dataset shows a strong seasonality component and has a nice, fine-grained detail to work with.

Load the Minimum Daily Temperatures Dataset

Download the Minimum Daily Temperatures dataset and place it in the current working directory with the filename “ daily-minimum-temperatures.csv “.

The code below will load and plot the dataset.

frompandasimportSeries frommatplotlibimportpyplot series = Series.from_csv('daily-minimum-temperatures.csv', header=0) series.plot() pyplot.show()

Running the example creates the following plot of the dataset.

Minimum Daily Temperature Dataset

Seasonal Adjustment with Differencing

A simple way to correct for aseasonalcomponent is to use differencing.

If there is a seasonal component at the level of one week, then we can remove it on an observation today by subtracting the value from last week.

In the case of the Minimum Daily Temperatures dataset, it looks like we have a seasonal component each year showing swing from summer to winter.

We can subtract the daily minimum temperature from the same day last year to correct for seasonality. This would require special handling of February 29th in leap years and would mean that the first year of data would not be available for modeling.

Below is an example of using the difference method on the daily data in Python.

frompandasimportSeries frommatplotlibimportpyplot series = Series.from_csv('daily-minimum-temperatures.csv', header=0) X = series.values diff = list() days_in_year = 365 for i in range(days_in_year, len(X)): value = X[i] - X[i - days_in_year] diff.append(value) pyplot.plot(diff) pyplot.show()

Running this example creates a new seasonally adjusted dataset and plots the result.

Differencing Sesaonal Adjusted Minimum Daily Temperature

There are two leap years in our dataset (1984 and 1988). They are not explicitly handled; this means that observations in March 1984 onwards the offset are wrong by one day, and after March 1988, the offsets are wrong by two days.

One option is to update the code example to be leap-day aware.

Another option is to consider that the temperature within any given period of the year is probably stable. Perhaps over a few weeks. We can shortcut this idea and consider all temperatures within a c