#### Overview of this book

This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you’ll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.
Title Page
Credits
Packt Upsell
Customer Feedback
Preface
Free Chapter
A Process for Success
Linear Regression - The Blocking and Tackling of Machine Learning
Logistic Regression and Discriminant Analysis
Advanced Feature Selection in Linear Models
More Classification Techniques - K-Nearest Neighbors and Support Vector Machines
Classification and Regression Trees
Neural Networks and Deep Learning
Cluster Analysis
Principal Components Analysis
Market Basket Analysis, Recommendation Engines, and Sequential Analysis
Creating Ensembles and Multiclass Classification
Time Series and Causality
Text Mining
R on the Cloud
R Fundamentals
Sources

## Univariate time series analysis

We will focus on two methods to analyze and forecast a single time series: exponential smoothing and autoregressive integrated moving average (ARIMA) models. We will start by looking at exponential smoothing models.

Like moving average models, exponential smoothing models use weights for past observations. But unlike moving average models, the more recent the observation the more weight it is given relative to the later ones. There are three possible smoothing parameters to estimate: the overall smoothing parameter, a trend parameter, and smoothing parameter. If no trend or seasonality is present, then these parameters become null.

The smoothing parameter produces a forecast with the following equation:

Yt+1 = α(Yt) + (1 – α)Yt-1 + (1-α)2Yt-2 +…, where 0 < α ≤ 1

In this equation, Yt is the value at the time T, and alpha (α) is the smoothing parameter. Algorithms optimize the alpha (and other parameters) by minimizing the errors, for example, sum of squared...