What is ACF in ARIMA?
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What is ACF in ARIMA?

What is ACF in ARIMA?

This recipe explains what is ACF in ARIMA

Recipe Objective

To calculate lag values for the Autoregression (AR) and Moving Average (MA) parameters, p and q respectively in ARIMA modelling, ACF (Autocorrelation function) is used. It is the coorelation between observation of a time series separated by k time units.

So this recipe is a short example on What is ACF in ARIMA. Let's get started.

Step 1 - Import the library

import numpy as np import pandas as pd from statsmodels.graphics.tsaplots import plot_acf import matplotlib.pyplot as plt

Let's pause and look at these imports. Numpy and pandas are general ones. Here, plot_acf and plt will help is plotting of ACF pattern of ARIMA model.

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 used one time series data from github. Also, we have set our index to date.

Now our dataset is ready.

Step 3 - Plotting ACF

plt.figure() plt.subplot(211) plot_acf(df, ax=plt.gca()) plt.show()

We have used plot_acf to simply plot our ACF model. By observing the plot, we can have an understanding of the lag between AR and MR terms of ARIMA model.

Step 4 - Lets look at our dataset now

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

Srcoll down the ipython file to visualize the results.

Clearly, an exponential decay in the seasonal pattern can be seen.

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