There are purpose-built systems to store and work with time series data. Some of these are even written in Go, including Prometheus and InfluxDB. However, some of the tooling that we have already utilized in the book is also suitable to handle time series. Specifically, github.com/kniren/gota/dataframe, gonum.org/v1/gonum/floats, and gonum.org/v1/gonum/mat can help us as we are working with time series data.
Take, for example, a dataset that includes a time series representing the number of international air passengers during the years 1949-1960 (available for download at https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/datasets/AirPassengers.csv):
$ head AirPassengers.csv time,AirPassengers 1949.0,112 1949.08333333,118 1949.16666667,132 1949.25,129 1949.33333333,121 1949.41666667,135 1949.5,148 1949.58333333,148 1949.66666667...
