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

Autocorrelation


Autocorrelation is a measure of the correlation between the lagged values of a time series. For example, r1 is the autocorrelation between yand yt-1; similarly, ris the autocorrelation between yand yt-2. This can be summarized in the following formula:

In the preceding formula, T is the length of the time series.

 

For example, say that we have the correlation coefficients, as shown in the following diagram:

To plot it, we get the following:

The following are some observations from this autocorrelation function plot:

  • r4 is higher than other lags, which is mostly because of a seasonal pattern
  • The blue lines are the indicators of whether correlations are significantly different from zero
  • Autocorrelation at lag 0 is always 1