How to create a lag plot for timeseries data?
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How to create a lag plot for timeseries data?

How to create a lag plot for timeseries data?

This recipe helps you create a lag plot for timeseries data

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Recipe Objective

Lag plots are most commonly used to look for patterns in time series data.

So this recipe is a short example on How to create a lag plot for timeseries data. Let's get started.

Step 1 - Import the library

import pandas as pd

Let's pause and look at these imports. Pandas is generally used for performing mathematical operation and preferably over arrays.

Step 2 - Setup the Data

df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv', parse_dates=['date']).set_index('date')

Here we have imported random time series dataset from github.

Now our dataset is ready.

Step 3 - Plotting Lag plot

pd.plotting.lag_plot(df, lag=1)

Using lag_plot, we are plotting our dataset. Lag here is set to be 1.

Step 4 - Let's look at our dataset now

Once we run the above code snippet, we will see:

Scroll down to the ipython file to look at the results.

This dataset has almost all the featurers of time variation. Lag clearly helps in understanding how these features are set in.

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