Book Image

Machine Learning Quick Reference

By : Rahul Kumar
Book Image

Machine Learning Quick Reference

By: Rahul Kumar

Overview of this book

Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Granger causality


In time series, we typically use univariate data. That is, we use a single series to predict its future values. Let's say that we are studying Google's stock price data, and we are asked to forecast the future values of stock prices. In this case, we will need historic data of Google's stock prices. Based on that, we will make predictions.

However, at times, we need multiple time series to make a forecast. But why is it that we need multiple time series? Any guesses?

The following graph shows Google's stock price data:

The answer is that we need to understand and explore the relationship between multiple time series as this can improve our forecast. For example, we have got correlated time series of GDP Deflator: Services and WPI: All Commodities, as follows:

 

It is quite evident that these two seem to carry a relationship. When we have to forecast GDP Deflator: Services, we can use WPI: All Commodities time series data as input. This is called Granger causality.

To put it more...