You're reading from Mastering Machine Learning Algorithms Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838820299

Length 798 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Authors (2):

Giuseppe Bonaccorso

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Table of Contents (28) Chapters

Preface

1. Machine Learning Model Fundamentals

2. Loss Functions and Regularization FREE CHAPTER

3. Introduction to Semi-Supervised Learning

4. Advanced Semi-Supervised Classification

5. Graph-Based Semi-Supervised Learning

6. Clustering and Unsupervised Models

7. Advanced Clustering and Unsupervised Models

8. Clustering and Unsupervised Models for Marketing

9. Generalized Linear Models and Regression

10. Introduction to Time-Series Analysis

11. Bayesian Networks and Hidden Markov Models

12. The EM Algorithm

13. Component Analysis and Dimensionality Reduction

14. Hebbian Learning

15. Fundamentals of Ensemble Learning

16. Advanced Boosting Algorithms

17. Modeling Neural Networks

18. Optimizing Neural Networks

19. Deep Convolutional Networks

20. Recurrent Neural Networks

21. Autoencoders

22. Introduction to Generative Adversarial Networks

23. Deep Belief Networks

24. Introduction to Reinforcement Learning

25. Advanced Policy Estimation Algorithms

26. Other Books You May Enjoy

27. Index

Time-series

In this chapter, we are going to briefly introduce the concepts of time-series and stochastic processes. As the topic is very wide, we are only discussing a few fundamental aspects and, at the same time, inviting the reader to refer to a complete book, such as Shumway R. H., Stoffer D. S., Time Series Analysis and Its Applications, Springer, 2017, for all the details.

The main concept of this chapter concerns the structure of a time-series. In this book, we are assuming that we are working with univariate series in the form:

Every value y_i depends implicitly on time (that is, y_i= y(i)); therefore, the series cannot be shuffled without losing information. If the values y_t are completely determined by a law (such as y_t = t²), the underlying process is described as deterministic. This is the case with many physical laws, but it's almost useless for us because the future can not be predicted without uncertainty. On the other hand, if every y_i is a...