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 the data provided by the United States Social Security Administration.

Getting ready

Download the Babies dataset from the book's GitHub repository at https://github.com/ipython-books/cookbook-data, and extract it in the current directory.

The data has been obtained from www.data.gov (http://catalog.data.gov/dataset/baby-names-from-social-security-card-applications-national-level-data-6315b).

How to do it...

1. We import the following packages:

   In [1]: import os
           import numpy as np
           import pandas as pd
           import matplotlib.pyplot as plt
           %matplotlib inline...