Time series is different from more traditional classification and regression predictive modeling problems.
The temporal structure adds an order to the observations. This imposed order means that important assumptions about the consistency of those observations needs to be handled specifically.
For example, when modeling, there are assumptions that the summary statistics of observations are consistent. In time series terminology, we refer to this expectation as the time series being stationary.
These assumptions can be easily violated in time series by the addition of a trend, seasonality, and other timedependent structures.
In this tutorial, you will discover how to check if your time series is stationary with Python.
After completing this tutorial, you will know:
 How to identify obvious stationary and nonstationary time series using line plot.
 How to spot check summary statistics like mean and variance for a change over time.
 How to use statistical tests with statistical significance to check if a time series is stationary.
Let’s get started.
Stationary Time Series
The observations in a stationary time series are not dependent on time.
Time series are stationary if they do not have trend or seasonal effects. Summary statistics calculated on the time series are consistent over time, like the mean or the variance of the observations.
When a time series is stationary, it can be easier to model. Statistical modeling methods assume or require the time series to be stationary to be effective.
Below is an example of the Daily Female Births dataset that is stationary.

from pandas import Series from matplotlib import pyplot series = Series.from_csv(‘dailytotalfemalebirths.csv’, header=0) series.plot() pyplot.show() 
Running the example creates the following plot.
NonStationary Time Series
Observations from a nonstationary time series show seasonal effects, trends, and other structures that depend on the time index.
Summary statistics like the mean and variance do change over time, providing a drift in the concepts a model may try to capture.
Classical time series analysis and forecasting methods are concerned with making nonstationary time series data stationary by identifying and removing trends and removing stationary effects.
Below is an example of the Airline Passengers dataset that is nonstationary, showing both trend and seasonal components.

from pandas import Series from matplotlib import pyplot series = Series.from_csv(‘internationalairlinepassengers.csv’, header=0) series.plot() pyplot.show() 
Running the example creates the following plot.
Types of Stationary Time Series
The notion of stationarity comes from the theoretical study of time series and it is a useful abstraction when forecasting.
There are some finergrained notions of stationarity that you may come across if you dive deeper into this topic. They are:
They are:
 Stationary Process: A process that generates a stationary series of observations.
 Stationary Model: A model that describes a stationary series of observations.
 Trend Stationary: A time series that does not exhibit a trend.
 Seasonal Stationary: A time series that does not exhibit seasonality.
 Strictly Stationary: A mathematical definition of a stationary process, specifically that the joint distribution of observations is invariant to time shift.
Stationary Time Series and Forecasting
Should you make your time series stationary?
Generally, yes.
If you have clear trend and seasonality in your time series, then model these components, remove them from observations, then train models on the residuals.
If we fit a stationary model to data, we assume our data are a realization of a stationary process. So our first step in an analysis should be to check whether there is any evidence of a trend or seasonal effects and, if there is, remove them.
— Page 122, Introductory Time Series with R.
Statistical time series methods and even modern machine learning methods will benefit from the clearer signal in the data.
But…
We turn to machine learning methods when the classical methods fail. When we want more or better results. We cannot know how to best model unknown nonlinear relationships in time series data and some methods may result in better performance when working with nonstationary observations or some mixture of stationary and nonstationary views of the problem.
The suggestion here is to treat properties of a time series being stationary or not as another source of information that can be used in feature engineering and feature selection on your time series problem when using machine learning methods.
Checks for Stationarity
There are many methods to check whether a time series (direct observations, residuals, otherwise) is stationary or nonstationary.
 Look at Plots: You can review a time series plot of your data and visually check if there are any obvious trends or seasonality.
 Summary Statistics: You can review the summary statistics for your data for seasons or random partitions and check for obvious or significant differences.
 Statistical Tests: You can use statistical tests to check if the expectations of stationarity are met or have been violated.
Above, we have already introduced the Daily Female Births and Airline Passengers datasets as stationary and nonstationary respectively with plots showing an obvious lack and presence of trend and seasonality components.
Next, we will look at a quick and dirty way to calculate and review summary statistics on our time series dataset for checking to see if it is stationary.
Summary Statistics
A quick and dirty check to see if your time series is nonstationary is to review summary statistics.
You can split your time series into two (or more) partitions and compare the mean and variance of each group. If they differ and the difference is statistically significant, the time series is likely nonstationary.
Next, let’s try this approach on the Daily Births dataset.
Daily Births Dataset
Because we are looking at the mean and variance, we are assuming that the data conforms to a Gaussian (also called the bell curve or normal) distribution.
We can also quickly check this by eyeballing a histogram of our observations.

from pandas import Series from matplotlib import pyplot series = Series.from_csv(‘dailytotalfemalebirths.csv’, header=0) series.hist() pyplot.show() 
Running the example plots a histogram of values from the time series. We clearly see the bell curvelike shape of the Gaussian distribution, perhaps with a longer right tail.
Next, we can split the time series into two contiguous sequences. We can then calculate the mean and variance of each group of numbers and compare the values.

from pandas import Series series = Series.from_csv(‘dailytotalfemalebirths.csv’, header=0) X = series.values split = len(X) / 2 X1, X2 = X[0:split], X[split:] mean1, mean2 = X1.mean(), X2.mean() var1, var2 = X1.var(), X2.var() print(‘mean1=%f, mean2=%f’ % (mean1, mean2)) print(‘variance1=%f, variance2=%f’ % (var1, var2)) 
Running this example shows that the mean and variance values are different, but in the same ballpark.

mean1=39.763736, mean2=44.185792 variance1=49.213410, variance2=48.708651 
Next, let’s try the same trick on the Airline Passengers dataset.
Airline Passengers Dataset
Cutting straight to the chase, we can split our dataset and calculate the mean and variance for each group.

from pandas import Series series = Series.from_csv(‘internationalairlinepassengers.csv’, header=0) X = series.values split = len(X) / 2 X1, X2 = X[0:split], X[split:] mean1, mean2 = X1.mean(), X2.mean() var1, var2 = X1.var(), X2.var() print(‘mean1=%f, mean2=%f’ % (mean1, mean2)) print(‘variance1=%f, variance2=%f’ % (var1, var2)) 
Running the example, we can see the mean and variance look very different.
We have a nonstationary time series.

mean1=182.902778, mean2=377.694444 variance1=2244.087770, variance2=7367.962191 
Well, maybe.
Let’s take one step back and check if assuming a Gaussian distribution makes sense in this case by plotting the values of the time series as a histogram.

from pandas import Series from matplotlib import pyplot series = Series.from_csv(‘internationalairlinepassengers.csv’, header=0) series.hist() pyplot.show() 
Running the example shows that indeed the distribution of values does not look like a Gaussian, therefore the mean and variance values are less meaningful.
This squashed distribution of the observations may be another indicator of a nonstationary time series.
Reviewing the plot of the time series again, we can see that there is an obvious seasonality component, and it looks like the seasonality component is growing.
This may suggest an exponential growth from season to season. A log transform can be used to flatten out exponential change back to a linear relationship.
Below is the same histogram with a log transform of the time series.

from pandas import Series from matplotlib import pyplot from numpy import log series = Series.from_csv(‘internationalairlinepassengers.csv’, header=0) X = series.values X = log(X) pyplot.hist(X) pyplot.show() pyplot.plot(X) pyplot.show() 
Running the example, we can see the more familiar Gaussianlike or Uniformlike distribution of values.
We also create a line plot of the log transformed data and can see the exponential growth seems diminished, but we still have a trend and seasonal elements.
We can now calculate the mean and standard deviation of the values of the log transformed dataset.

from pandas import Series from matplotlib import pyplot from numpy import log series = Series.from_csv(‘internationalairlinepassengers.csv’, header=0) X = series.values X = log(X) split = len(X) / 2 X1, X2 = X[0:split], X[split:] mean1, mean2 = X1.mean(), X2.mean() var1, var2 = X1.var(), X2.var() print(‘mean1=%f, mean2=%f’ % (mean1, mean2)) print(‘variance1=%f, variance2=%f’ % (var1, var2)) 
Running the examples shows mean and standard deviation values for each group that are again similar, but not identical.
Perhaps, from these numbers alone, we would say the time series is stationary, but we strongly believe this to not be the case from reviewing the line plot.

mean1=5.175146, mean2=5.909206 variance1=0.068375, variance2=0.049264 
This is a quick and dirty method that may be easily fooled.
We can use a statistical test to check if the difference between two samples of Gaussian random variables is real or a statistical fluke. We could explore statistical significance tests, like the Student ttest, but things get tricky because of the serial correlation between values.
In the next section, we will use a statistical test designed to explicitly comment on whether a univariate time series is stationary.
Augmented DickeyFuller test
Statistical tests make strong assumptions about your data. They can only be used to inform the degree to which a null hypothesis can be accepted or rejected. The result must be interpreted for a given problem to be meaningful.
Nevertheless, they can provide a quick check and confirmatory evidence that your time series is stationary or nonstationary.
The Augmented DickeyFuller test is a type of statistical test called a unit root test.
The intuition behind a unit root test is that it determines how strongly a time series is defined by a trend.
There are a number of unit root tests and the Augmented DickeyFuller may be one of the more widely used. It uses an autoregressive model and optimizes an information criterion across multiple different lag values.
The null hypothesis of the test is that the time series can be represented by a unit root, that it is not stationary (has some timedependent structure). The alternate hypothesis (rejecting the null hypothesis) is that the time series is stationary.
 Null Hypothesis (H0): If accepted, it suggests the time series has a unit root, meaning it is nonstationary. It has some time dependent structure.
 Alternate Hypothesis (H1): The null hypothesis is rejected; it suggests the time series does not have a unit root, meaning it is stationary. It does not have timedependent structure.
We interpret this result using the pvalue from the test. A pvalue below a threshold (such as 5% or 1%) suggests we accept the null hypothesis (nonstationary), otherwise it suggests we reject the null hypothesis (stationary).
Below is an example of calculating the Augmented DickeyFuller test on the Daily Female Births dataset. The statsmodels library provides the adfuller() function that implements the test.

from pandas import Series from statsmodels.tsa.stattools import adfuller series = Series.from_csv(‘dailytotalfemalebirths.csv’, header=0) X = series.values result = adfuller(X) print(‘ADF Statistic: %f’ % result[0]) print(‘pvalue: %f’ % result[1]) print(‘Critical Values:’) for key, value in result[4].items(): print(‘t%s: %.3f’ % (key, value)) 
Running the example prints the test statistic value of 4. The more negative this statistic, the more likely we are to reject the null hypothesis (we have a stationary dataset).
As part of the output, we get a lookup table to help determine the ADF statistic. We can see that our statistic value of 4 is less than the value of 3.449 at 1%.
This suggests that we can reject the null hypothesis with a significance level of less than 1% (i.e. a low probability that the result is a statistical fluke).
Rejecting the null hypothesis means that the process has no unit root, and in turn that the time series is stationary or does not have timedependent structure.

ADF Statistic: 4.808291 pvalue: 0.000052 Critical Values: 5%: 2.870 1%: 3.449 10%: 2.571 
We can perform the same test on the Airline Passenger dataset.

from pandas import Series from statsmodels.tsa.stattools import adfuller series = Series.from_csv(‘internationalairlinepassengers.csv’, header=0) X = series.values result = adfuller(X) print(‘ADF Statistic: %f’ % result[0]) print(‘pvalue: %f’ % result[1]) print(‘Critical Values:’) for key, value in result[4].items(): print(‘t%s: %.3f’ % (key, value)) 
Running the example gives a different picture than the above. The test statistic is positive, meaning we are much less likely to reject the null hypothesis (it looks nonstationary).
Comparing the test statistic to the critical values, it looks like we would have to accept the null hypothesis that the time series is nonstationary and does have timedependent structure.

ADF Statistic: 0.815369 pvalue: 0.991880 Critical Values: 5%: 2.884 1%: 3.482 10%: 2.579 
Let’s log transform the dataset again to make the distribution of values more linear and better meet the expectations of this statistical test.

from pandas import Series from statsmodels.tsa.stattools import adfuller from numpy import log series = Series.from_csv(‘internationalairlinepassengers.csv’, header=0) X = series.values X = log(X) result = adfuller(X) print(‘ADF Statistic: %f’ % result[0]) print(‘pvalue: %f’ % result[1]) for key, value in result[4].items(): print(‘t%s: %.3f’ % (key, value)) 
Running the example shows a negative value for the test statistic.
We can see that the value is larger than the critical values, again, meaning that we can accept the null hypothesis and in turn that the time series is nonstationary.

ADF Statistic: 1.717017 pvalue: 0.422367 5%: 2.884 1%: 3.482 10%: 2.579 
Summary
In this tutorial, you discovered how to check if your time series is stationary with Python.
Specifically, you learned:
 The importance of time series data being stationary for use with statistical modeling methods and even some modern machine learning methods.
 How to use line plots and basic summary statistics to check if a time series is stationary.
 How to calculate and interpret statistical significance tests to check if a time series is stationary.
Do you have any questions about stationary and nonstationary time series, or about this post?
Ask your questions in the comments below and I will do my best to answer.