Let's remember the basics. What is stationary data? Data is stationary if its statistical properties, such as the mean, variance, and autocorrelation function, do not depend on time. 

For example, white noise process is stationary. This process doesn't have any trends and other time series patterns, samples are not correlated with each other and variance equals 1 for all samples. 


There is no particular type of data that will always be stationary. For example, we cannot say that purchases in an online store will necessarily be stationary (quite the opposite, mostly non-stationary). But if we start to examine accident statistics during some short period, we will most likely find that these data are stationary, and there is no apparent dependence on each other.

Most time series are non-stationary. That is why some methods allow you to transform data into stationary ones. In the next section, you will learn how to implement this.

Why does the data need to be stationary? Because only stationary data contains characteristics that must be observed when using the most popular predictive models. Moreover, even just from the point of view of logic, if the data does not obey any particular law and has different characteristics at different points of time, then such data is very difficult to predict


What can be done with thousands of online store purchase records? How can we analyze this data and predict its growth? In this course, you will learn what parameters we can analyze in time series and how to create predictive models. Let's get started!

Start from the introduction to time series to move on to more in-depth topics. Find out what it is, how often time series occur in real business, how to analyze them and make forecasts.

What is the difference between trend and seasonality? Is the data I'm working with stationary or not? In this section, you will learn how to analyze characteristics of time series using Python!


Learn more about time series through… their visualization! Explore your data with 2D and 3D graphs.


What will be our first step? Let's start with stationary models! Create your first predictive model that will forecast time series.

Most of the data you work with is non-stationary. Learn how to process such data and what models to use to predict it.

Let's dive into a variety of interesting tasks that you may encounter while working with time series! Find out how big these challenges are and what models we can use to solve them.

Examples of Stationary Time Series

Examples of Stationary Time Series