Book Image

Mastering Machine Learning Algorithms. - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms. - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
Table of Contents (28 chapters)
26
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27
Index

Introduction to linear models for time-series

In this section, we are going to employ an artificial time-series to show some common linear models for time-series. The goal is not to provide an exhaustive explanation (which would require an entire book), but to introduce the reader to this kind of modeling method. The reader who is interested in the topic (and would like to read a complete mathematical background) can check Shumway R. H., Stoffer D. S., Time Series Analysis and Its Applications, Springer, 2017.

A time-series containing 100 observations with a frequency of 0.5 (2 observations per time instant) is generated by the following snippet:

import numpy as np
x = np.expand_dims(np.arange(0, 50, 0.5), axis=1)
y = np.sin(5.*x) + np.random.normal(0.0, 0.5, size=x.shape)
y = np.squeeze(y)

A graphical representation is shown in the following figure:

Synthetic time-series with 100 observations

This time-series has no particular characteristic except...