11. Time Series and Causality | Mastering Machine Learning with R

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Table Of Contents

Mastering Machine Learning with R

By : Cory Lesmeister

4.3 (6)

Mastering Machine Learning with R

4.3 (6)

By: Cory Lesmeister

Overview of this book

Machine learning is a field of Artificial Intelligence to build systems that learn from data. Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning to your data. The book starts with introduction to Cross-Industry Standard Process for Data Mining. It takes you through Multivariate Regression in detail. Moving on, you will also address Classification and Regression trees. You will learn a couple of “Unsupervised techniques”. Finally, the book will walk you through text analysis and time series. The book will deliver practical and real-world solutions to problems and variety of tasks such as complex recommendation systems. By the end of this book, you will gain expertise in performing R machine learning and will be able to build complex ML projects using R and its packages.

Preface

Preface

Machine learning defined

Machine learning caveats

Failure to engineer features

Overfitting and underfitting

Causality

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. A Process for Success

1. A Process for Success

The process

Business understanding

Data understanding

Data preparation

Modeling

Evaluation

Deployment

Algorithm flowchart

Summary

2. Linear Regression – The Blocking and Tackling of Machine Learning

2. Linear Regression – The Blocking and Tackling of Machine Learning

Univariate linear regression

Multivariate linear regression

Other linear model considerations

Summary

3. Logistic Regression and Discriminant Analysis

3. Logistic Regression and Discriminant Analysis

Classification methods and linear regression

Logistic regression

Model selection

Summary

4. Advanced Feature Selection in Linear Models

4. Advanced Feature Selection in Linear Models

Regularization in a nutshell

Business case

Modeling and evaluation

Model selection

Summary

5. More Classification Techniques – K-Nearest Neighbors and Support Vector Machines

5. More Classification Techniques – K-Nearest Neighbors and Support Vector Machines

K-Nearest Neighbors

Support Vector Machines

Business case

Feature selection for SVMs

Summary

6. Classification and Regression Trees

6. Classification and Regression Trees

Introduction

An overview of the techniques

Business case

Summary

7. Neural Networks

7. Neural Networks

Neural network

Deep learning, a not-so-deep overview

Business understanding

Data understanding and preparation

Modeling and evaluation

An example of deep learning

Summary

8. Cluster Analysis

8. Cluster Analysis

Hierarchical clustering

K-means clustering

Gower and partitioning around medoids

Data understanding and preparation

Modeling and evaluation

Summary

9. Principal Components Analysis

9. Principal Components Analysis

An overview of the principal components

Modeling and evaluation

Summary

10. Market Basket Analysis and Recommendation Engines

10. Market Basket Analysis and Recommendation Engines

An overview of a market basket analysis

Business understanding

Data understanding and preparation

Modeling and evaluation

An overview of a recommendation engine

Business understanding and recommendations

Data understanding, preparation, and recommendations

Modeling, evaluation, and recommendations

Summary

11. Time Series and Causality

11. Time Series and Causality

Univariate time series analysis

Modeling and evaluation

Summary

12. Text Mining

12. Text Mining

Text mining framework and methods

Topic models

Modeling and evaluation

Summary

A. R Fundamentals

A. R Fundamentals

Introduction

Getting R up and running

Using R

Data frames and matrices

Summary stats

Installing and loading the R packages

Summary

Index

Index

Modeling and evaluation

For the modeling and evaluation step, we will focus on three tasks. The first is to produce a univariate forecast model applied to just the surface temperature. The second is developing a regression model of the surface temperature based on itself and carbon emissions. Finally, we will try and discover if emissions Granger-cause the surface temperature anomalies.

Univariate time series forecasting

With this task, the objective is to produce a univariate forecast for the surface temperature, focusing on choosing either a Holt linear trend model or an ARIMA model. As discussed previously, the temperature anomalies start to increase around 1970. Therefore, I recommend looking at it from this point to the present. The following code creates the subset and plots the series:

> T2 = window(T, start=1970)

> plot(T2)

Our train and test sets will be through 2007, giving us eight years of data to evaluate for the selection. Once again, the window() function allows us to...

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