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

Chapter 5. Time Series Analysis

In this chapter, we will take a look at time series analysis and learn several ways of observing and capturing an event at different points in time. We will introduce the concept of white noise and learn about its detection in a series

We will take the time series data and compute the differences between the consecutive observations, which will lead to the formation of a new series. These concepts will help us deep dive into time series analysis and help us build a deeper understanding around it.

In this chapter, we will cover the following topics:

  • Introduction to time series analysis
  • White noise
  • Random walk
  • Autoregression
  • Autocorrelation
  • Stationarity
  • Differencing
  • AR model
  • Moving average model
  • Autoregressive integrated moving average
  • Optimization of parameters
  • Anomaly detection