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Autocorrelation in time series data

In the field of time series analysis, autocorrelation refers to the correlation of a time series with a lagged version of itself. For example, an autocorrelation of order 3 returns the correlation between a time series and its own values lagged by 3 time points.

It is common to use the autocorrelation (ACF) plot, also known as self-autocorrelation, to visualize the autocorrelation of a time-series. The plot_acf() function in the statsmodels library can be used to measure and plot the autocorrelation of a time series.

This is a part of the course

“Visualizing Time Series Data in Python”

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Exercise instructions

  • Import tsaplots from statsmodels.graphics.
  • Use the plot_acf() function from tsaplots to plot the autocorrelation of the 'co2' column in co2_levels.
  • Specify a maximum lag of 24.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import required libraries
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
from ____ import ____

# Display the autocorrelation plot of your time series
fig = ____(co2_levels[____], lags=____)

# Show plot
plt.show()

This exercise is part of the course

Visualizing Time Series Data in Python

IntermediateSkill Level
4.7+
15 reviews

Visualize seasonality, trends and other patterns in your time series data.

You will go beyond summary statistics by learning about autocorrelation and partial autocorrelation plots. You will also learn how to automatically detect seasonality, trend and noise in your time series data.

Exercise 1: Autocorrelation and Partial autocorrelationExercise 2: Autocorrelation in time series data
Exercise 3: Interpret autocorrelation plotsExercise 4: Partial autocorrelation in time series dataExercise 5: Interpret partial autocorrelation plotsExercise 6: Seasonality, trend and noise in time series dataExercise 7: Time series decompositionExercise 8: Plot individual componentsExercise 9: A quick reviewExercise 10: Visualize the airline datasetExercise 11: Analyze the airline datasetExercise 12: Time series decomposition of the airline dataset

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