CANCEL

Subscription

0

Cart

You have no products in your basket yet

Save more on your purchases!
Savings automatically calculated. No voucher code required

Account

eBook

Print

$38.99
Subscription

$15.99
Monthly
eBook

Print

$38.99
Subscription

$15.99
Monthly
Download this book in **EPUB** and **PDF** formats

Access this title in our online reader with advanced features

- Perform time-series analysis and forecasting using R packages such as forecast and h2o
- Develop models and find patterns to create visualizations using the TSstudio and plotly packages
- Learn statistics and implement time-series methods with the help of examples

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.

Visualize time-series data and derive useful insights
Study auto-correlation and understand statistical techniques
Use time-series analysis tools from the stats, TSstudio, and forecast packages
Explore and identify seasonal and correlation patterns
Work with different time-series formats in R
Discover time-series models such as ARIMA, Holt-Winters, and more
Evaluate high-performance forecasting solutions

Download this book in **EPUB** and **PDF** formats

Access this title in our online reader with advanced features

Publication date :
May 31, 2019

Length
448 pages

Edition :
1st Edition

Language :
English

ISBN-13 :
9781788629157

Category :

Languages :

Concepts :

Preface

1. Introduction to Time Series Analysis and R

2. Working with Date and Time Objects

3. The Time Series Object

4. Working with zoo and xts Objects

5. Decomposition of Time Series Data

6. Seasonality Analysis

7. Correlation Analysis

8. Forecasting Strategies

9. Forecasting with Linear Regression

10. Forecasting with Exponential Smoothing Models

11. Forecasting with ARIMA Models

12. Forecasting with Machine Learning Models

13. Other Books You May Enjoy

No reviews found

How do I buy and download an eBook?

How can I make a purchase on your website?

Where can I access support around an eBook?

What eBook formats do Packt support?

What are the benefits of eBooks?

What is an eBook?