(Python Data Visualisation Tutorials)
Python Data Visualisation for Business Analyst – How to do Autocorrelation (ACF) and Partial Autocorrelation (PACF) Plot in Python
In this data visualisation tutorial, How to do Autocorrelation (ACF) and Partial Autocorrelation (PACF) Plot in Python.
The ACF plot shows the correlation of the time series with its own lags. Each vertical line (on the autocorrelation plot) represents the correlation between the series and its lag starting from lag 0. The blue shaded region in the plot is the significance level. Those lags that lie above the blue line are the significant lags.
So how to interpret this?
For AirPassengers, we see upto 14 lags have crossed the blue line and so are significant. This means, the Air Passengers traffic seen up to 14 years back has an influence on the traffic seen today.
PACF on the other had shows the autocorrelation of any given lag (of time series) against the current series, but with the contributions of the lags-in-between removed.
Setup
Run this once before the plot’s code. The individual charts, however, may redefine its own aesthetics.
/* !pip install brewer2mpl */
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import warnings; warnings.filterwarnings(action='once')
large = 22; med = 16; small = 12
params = {'axes.titlesize': large,
'legend.fontsize': med,
'figure.figsize': (16, 10),
'axes.labelsize': med,
'axes.titlesize': med,
'xtick.labelsize': med,
'ytick.labelsize': med,
'figure.titlesize': large}
plt.rcParams.update(params)
plt.style.use('seaborn-whitegrid')
sns.set_style("white")
%matplotlib inline
/* Version */
print(mpl.__version__)
print(sns.__version__)How to do Autocorrelation (ACF) and Partial Autocorrelation (PACF) Plot in Python
Download Code:
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
/* Import Data */
df = pd.read_csv('AirPassengers.csv')
/* Draw Plot */
fig, (ax1, ax2) = plt.subplots(1, 2,figsize=(16,6), dpi= 80)
plot_acf(df.traffic.tolist(), ax=ax1, lags=50)
plot_pacf(df.traffic.tolist(), ax=ax2, lags=20)
/* Decorate */
/* lighten the borders */
ax1.spines["top"].set_alpha(.3); ax2.spines["top"].set_alpha(.3)
ax1.spines["bottom"].set_alpha(.3); ax2.spines["bottom"].set_alpha(.3)
ax1.spines["right"].set_alpha(.3); ax2.spines["right"].set_alpha(.3)
ax1.spines["left"].set_alpha(.3); ax2.spines["left"].set_alpha(.3)
/* font size of tick labels */
ax1.tick_params(axis='both', labelsize=12)
ax2.tick_params(axis='both', labelsize=12)
plt.show()
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