Lagged Scatterplot Calculator
Visualize the Correlation in Time Series Data with Our Lagged Scatterplot Calculator
This page exists due to the efforts of the following people:
 | Timur
|
This content is licensed under Creative Commons Attribution/Share-Alike License 3.0 (Unported). That means you may freely redistribute or modify this content under the same license conditions and must attribute the original author by placing a hyperlink from your site to this work https://planetcalc.com/7900/. Also, please do not modify any references to the original work (if any) contained in this content.
Our lagged scatterplot calculator is a powerful tool for analyzing the correlation between time series data. With the ability to display up to six lag plots, it provides a comprehensive view of the relationships between different points in the time series.
To use the calculator, simply enter your time series data and fill the lag plots boxes you would like to display. For your convenience, you can plot up to six lag functions on a single chart. They will differ by color and used marker symbol. Just enter the lag value into the corresponding marker box. The calculator will generate the scatterplots in just a few seconds, allowing you to quickly and easily identify patterns and correlations.
Lagged scatterplots are a useful tool in time series analysis, providing insight into the relationships between different data points and helping to identify trends and patterns. Whether you're a student, researcher, or data analyst, our lagged scatterplot calculator is a valuable resource for analyzing and visualizing time series data. Try it now and get a clearer understanding of your data.
You can find more theory below the calculator
Lagged scatterplot
Time Series
| Time | Value | ||
|---|---|---|---|
Lagged Scatterplot
What is lagged scatterplot or lag plot? It is the plot of lag function which can be defined for arbitrary k as follows:
,
where is the i-th value of time series.
Thus we are plotting the time series against itself offset in time, for example, . That is, both axis are for time series values.
This is the simplest graphical way to detect autocorrelation (the correlation of a time series with its own past and future values). A random scattering of points in the lagged scatterplot suggests little or no autocorrelation. On the opposite side, alignment or any other organized curvature in dots' pattern might suggest dependence between time-separated values. And one of the advantages of the lagged scatterplot is that it can show dependence regardless of the form (i.e., non-linear).
For example, the default time series in the calculator below was obtained by noising the sine function using Noisy function generator. And, as you can see, the lag plot definitely suggests that there is a dependence.
Comments