Time series data has unique features and a special signature that distinguishes it from other types of data. Among those features, you can find the series timestamp (or the series index), the series frequency and cycle, and the time interval in which the data was captured. We will discuss these features in detail in this chapter. These sets of features, as you will see throughout this book, are more than just a convenient data structure, as they have a meaningful statistical application for both a descriptive and predictive analysis of time series data. R provides several classes for representing time series objects for a variety of applications. Among those classes, `ts` is one of the main formats for time series data in R, mainly due to its simplicity and the wide adoption of this class by the main packages in R for time series analysis, for example, the...

#### Hands-On Time Series Analysis with R

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#### Hands-On Time Series Analysis with R

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#### 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

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