Overview of this book

Time-series analysis is the art of extracting meaningful insights from, and revealing patterns in, time-series data using statistical and data visualization approaches. These insights and patterns can then be utilized to explore past events and forecast future values in the series. This book explores the basics of time-series analysis with R and lays the foundation you need to build forecasting models. You will learn how to preprocess raw time-series data and clean and manipulate data with packages such as stats, lubridate, xts, and zoo. You will analyze data using both descriptive statistics and rich data visualization tools in R including the TSstudio, plotly, and ggplot2 packages. The book then delves into traditional forecasting models such as time-series linear regression, exponential smoothing (Holt, Holt-Winter, and more) and Auto-Regressive Integrated Moving Average (ARIMA) models with the stats and forecast packages. You'll also work on advanced time-series regression models with machine learning algorithms such as random forest and Gradient Boosting Machine using the h2o package. By the end of this book, you will have developed the skills necessary for exploring your data, identifying patterns, and building a forecasting model using various traditional and machine learning methods.
Table of Contents (14 chapters)
Preface
Free Chapter
Introduction to Time Series Analysis and R
Working with Date and Time Objects
The Time Series Object
Working with zoo and xts Objects
Decomposition of Time Series Data
Seasonality Analysis
Correlation Analysis
Forecasting Strategies
Forecasting with Linear Regression
Forecasting with Exponential Smoothing Models
Forecasting with ARIMA Models
Forecasting with Machine Learning Models
Other Books You May Enjoy

Seasonality types

The series frequency, as we saw in the preceding chapters, defines the amounts of intervals in a single cycle unit of the series. The term cycle here refers to constant cycles, and should not be confused with the term cycle component, which was introduced in the previous chapter. In addition, the frequency units are ordered and repeated in the same order. For example, January and December would always be the first and last units, respectively, for a calendric series with a monthly frequency. A seasonal pattern exists in a time series whenever we can tie a repeated event in the series to a specific frequency unit, for example, the average temperature in New York during the month of January, or the average number of passengers in the London underground between 8 a.m. and 9 a.m. Hence, there is a strong relationship between seasonal pattern and the frequency of...