I am a strong believer in “learning by doing” philosophy.
Data science is an applied field, so you need to get your feet wet to learn something. One can read all the “how to” tutorials on swimming, but at some point, they do have to test the water.
Beginners in data science often get caught into the impression that they have to learn everything under the sun before they can do a project. Wrong! I believe people can learn faster not by reading stuff but by doing small bits and pieces of projects.
In this article I want you to learn how to fit a time series forecasting model ARIMA — which, for many, is an intimidating algorithm. In this article, you will learn it in just 5 easy steps and make real forecasts. You are not going to build a Ferrari, but I’m sure you will learn to build a car that you can take to the streets.
Let’s roll the sleeves.
For this demo, we are going to use a forecasting package calledfpp2 in R programming environment. Let’s load that package.
