Hướng dẫn rolling correlation in python

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Correlation generally determines the relationship between two variables. The rolling correlation measure the correlation between two-time series data on a rolling window Rolling correlation can be applied to a specific window width to determine short-term correlations. 

Calculating Rolling Correlation in Python

Let’s use sales data of two products A and B in the last 60 months to calculate the rolling correlation. Pandas package provides a function called rolling.corr[] to calculate the rolling correlation.

Syntax:

data1.rolling[width].corr[data2]

Where, 

• data1, data2 – data/column of interest [type series]
• width – Rolling window width [int]

Note: The width of the rolling window should be 3 or greater in order to calculate correlations.

Data Used:  

Python3

import pandas as pd

data = pd.read_csv['product_sales.csv']

print[data.head[10]]

print[data.columns]

Output:

Example 2:

Here, we used the window width of 6, which shows the successive 6 months rolling correlation. We could see a significant correlation between two products sales any sudden dip or rise in correlation signals an unusual event, that caused the dip.

Python3

data['Product A'].rolling[6].corr[data['Product B']]

k = 1

for i, j in enumerate[data['Product A'].rolling[6].corr[data['Product B']]]:

    if [i >= 5 and i < 12]:

        print[f'The correlation in sales during months\

        {k} through {i+1} is {j}']

        i = 0

        k += 1

Output:

Now’s let us try the same for 3-month correlation as shown below,

Example 3:

Python3

data['Product A'].rolling[3].corr[data['Product B']]

k = 1

for i, j in enumerate[data['Product A'].rolling[3].corr[data['Product B']]]:

    if [i >= 3 and i < 12]:

        print[

            f'The correlation in sales during months {k} \

            through {i+1} is {j}']

        i = 0

        k += 1

Output: