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

Autoregressive integrated moving average


An autoregressive integrated moving average (ARIMA) model is a combination of the following elements:

  • Autoregressive operator: We have already learned what this means; just to reiterate, it is the lags of the stationarized series. It is denoted by p, which is nothing but the number of autoregressive terms. The PACF plot yields this component.
  • Integration operator: A series that needs to be differenced to be made stationary is said to be an integrated version of a stationary series. It is denoted by d, which is the amount of differencing that is needed to transform the nonstationary time series into a stationary one. This is done by subtracting the observation from the current period from the previous one. If this has been done only once to the series, it is called first differenced. This process eliminates the trend out of the series that is growing at a constant rate. In this case, the series is growing at an increasing rate, and the differenced series...