Book Image

Machine Learning With Go

Book Image

Machine Learning With Go

Overview of this book

The mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but it also helps the reader understand how machine learning workflows are being applied in real-world scenarios. Machine Learning with Go shows readers how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives readers patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization. The readers will begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources. Readers will then develop a solid statistical toolkit that will allow them to quickly understand gain intuition about the content of a dataset. Finally, the readers will gain hands-on experience implementing essential machine learning techniques (regression, classification, clustering, and so on) with the relevant Go packages. Finally, the reader will have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations.
Table of Contents (11 chapters)

Auto-regressive models for forecasting

The first category of models that we are going to use to try and forecast our time series are called auto-regressive (AR) models. As already mentioned, we try to model a data point in our time series based on one or more previous points in the series. We are, thus, modeling the time series using the time series itself. This use of the series itself is what distinguishes AR methods from the more general regression methods discussed in Chapter 4, Regression.

Auto-regressive model overview

You will often see AR models referred to as AR(1), AR(2), and so on. These numbers correspond to the order of the AR model or process you are using to model the time series, and it is this order that you...