Time Series Episode 6: Battle of forecasting algorithms in “Darts”

Which algorithm can better forecast the Sunspots?

Image generated with Microsoft Copilot — “A boy playing darts with the sun”

Introduction

Hi there! Happy to see you again in this series of articles, where we discuss about Time Series, theory and examples.

In the previous articles we discussed about ARIMA-family models for forecasting and working examples of how to apply them, based on my experience from multiple projects so far.

In the last article, though, I presented the “Darts” library in Python which contains lots of forecasting algorithms, making it very easy to do feature engineering and apply and compare easily whatever you want, as well as evaluating results in the end (you can learn more here).

So, in this story I will work on a dataset that we had discussed about, but now trying different forecasting algorithms from Darts in order for you to get an understanding of how this library works and how to easily train 7 different models.

You ready? Let’s start!

Step-by-Step Working Example

We begin with the dataset described in my second hands-on tutorial: