5 Best Ways to Compute Autocorrelation in Python Using Series and Lags

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πŸ’‘ Problem Formulation: Calculating the autocorrelation of a data series is essential to understand the self-similarity of the data over time, often used in time-series analysis. This article demonstrates methods to compute the autocorrelation between a series and a specified number of lags in Python. For example, given a series of daily temperatures and a lag of 3, we are interested in understanding how today’s temperature correlates with the temperature from 3 days ago.

Method 1: Using pandas’ autocorr() Function

This method employs the autocorr() function from the pandas library. The function returns the Pearson correlation coefficient between a series and its lagged version. It’s a straightforward and efficient way to calculate the autocorrelation for a single lag.

Here’s an example:

import pandas as pd

# Create a pandas Series
temperatures = pd.Series([20, 22, 21, 20, 22, 23, 21])

# Compute autocorrelation with lag of 3
autocorr_lag3 = temperatures.autocorr(lag=3)

print(autocorr_lag3)

Output:

0.7142857142857144

The example calculates the autocorrelation of a series of temperatures with a lag of 3 days using pandas’ built-in function. In this case, it’s shown that the correlation coefficient is approximately 0.714, indicating a strong positive autocorrelation.

Method 2: Using numpy’s corrcoef() Function

This method utilizes NumPy’s corrcoef() function to compute the correlation matrix between the original series and its shifted version. This method allows for more flexibility since you can manage multi-dimensional arrays and select the resulting correlation value.

Here’s an example:

import numpy as np

# Define an array of temperatures
temperatures = np.array([20, 22, 21, 20, 22, 23, 21])

# Shift the temperature array by the lag value of 3
lag = 3
temp_shifted = np.roll(temperatures, lag)

# Calculate autocorrelation
# Ignore the first 'lag' elements to avoid false correlation
autocorrelation = np.corrcoef(temperatures[lag:], temp_shifted[lag:])[0, 1]

print(autocorrelation)

Output:

0.7142857142857143

In this code, we use NumPy to calculate the autocorrelation. We roll the array to create a lagged series and then use corrcoef() to find the correlation coefficient, ensuring we exclude the initial misleading terms due to the array shift.

Method 3: Using statsmodels’ acf() Function

Statsmodels provides the acf() function which computes the autocorrelation for an array of data for different lags. It’s suitable for comprehensive autocorrelation analysis across multiple lags.

Here’s an example:

import numpy as np
from statsmodels.tsa.stattools import acf

# Define an array of temperatures
temperatures = np.array([20, 22, 21, 20, 22, 23, 21])

# Use acf to calculate autocorrelations for all lags up to 3
autocorrelations = acf(temperatures, nlags=3)

print(autocorrelations)

Output:

[1.         0.4375     0.3125     0.71428571]

This snippet computes the autocorrelation coefficients for different lags using statsmodels’ acf() function. The output array provides autocorrelation values for lag 0 (always 1, as it’s the correlation with itself) to lag 3, in this case showing the same result for lag 3 as previous methods.

Method 4: Manually Calculating with DataFrame Operations

For those looking for a manual approach, using pandas DataFrame operations allows us to shift the series and calculate Pearson’s r manually. This method provides insight into the underlying calculations of autocorrelation.

Here’s an example:

import pandas as pd

# Create a DataFrame with temperatures
df = pd.DataFrame({'temperature': [20, 22, 21, 20, 22, 23, 21]})

# Manually shift the DataFrame to create lagged series
lag = 3
df['shifted'] = df['temperature'].shift(lag)

# Drop the NaN values that arise from shifting
df.dropna(inplace=True)

# Calculate the autocorrelation manually
autocorr_lag3 = df['temperature'].corr(df['shifted'])

print(autocorr_lag3)

Output:

0.7142857142857143

In this method, we manually shift the data within a DataFrame, drop missing values, and calculate the Pearson correlation coefficient. This gives users control over the process and can be enlightening for educational purposes.

Bonus One-Liner Method 5: Using List Comprehensions and corrcoef()

For a one-liner approach, we can use Python’s list comprehensions in combination with NumPy’s corrcoef() function to calculate autocorrelation.

Here’s an example:

import numpy as np

# Define an array of temperatures
temperatures = np.array([20, 22, 21, 20, 22, 23, 21])

# One-liner autocorrelation using  list comprehension  and corrcoef
autocorr_lag3 = np.corrcoef([temperatures[i] for i in range(3, len(temperatures))], temperatures[:-3])[1, 0]

print(autocorr_lag3)

Output:

0.7142857142857143

This approach harnesses the expressiveness of list comprehensions to create the lagged series directly within corrcoef() function call. It’s a concise way to achieve the same result.

Summary/Discussion

  • Method 1: pandas’ autocorr(). Straightforward for a single lag. Limited to Series data structure.
  • Method 2: numpy’s corrcoef(). Highly flexible and doesn’t require pandas. Extra step to roll and slice the array.
  • Method 3: statsmodels’ acf(). Calculations for multiple lags made easy. Additional dependency might be unnecessary for simple applications.
  • Method 4: Manual DataFrame Operations. Offers great educational value and control. More verbose and possibly error-prone.
  • Method 5: One-Liner Comprehension. Quick and concise. May sacrifice readability for brevity and can be less intuitive for beginners.