Computing the autocorrelation of a time series

The autocorrelation of a time series can inform us about repeating patterns or serial correlation. The latter refers to the correlation between the signal at a given time and at a later time. The analysis of the autocorrelation can thereby inform us about the timescale of the fluctuations. Here, we use this tool to analyze the evolution of baby names in the US, based on data provided by the United States Social Security Administration.

How to do it...

1. We import the following packages:

   >>> import os
       import numpy as np
       import pandas as pd
       import matplotlib.pyplot as plt
       %matplotlib inline

2. We download the Babies dataset (available on the GitHub data repository of the book) using the requests third-party package. The dataset was obtained initially from the data.gov website (https://catalog.data.gov/dataset/baby-names-from-social-security-card-applications-national-level-data). We extract the archive locally in the babies subdirectory...