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Working with Date and Time Objects

The main attribute of time series data is its timestamp, which could be a date object, time object, or other index format depending on the series frequency. Typically while loading raw data, it is not trivial to have the date or time object formatted and ready to use. Therefore, it is most likely that the raw data may require some reformatting before you are able to transform your data into time series format. The ability to work with time and date objects is an essential part of the data preparation process. In this chapter, we will introduce a set of tools and applications for dealing with those objects, starting with R's built-in tools and classes from the base package and moving to the advanced applications of the lubridate package.

In this chapter, we will cover the following topics:

The date and time formats
Date and time objects...

Key benefits

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

Description

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.

Who is this book for?

Hands-On Time Series Analysis with R is ideal for data analysts, data scientists, and R developers looking to perform time-series analysis to predict outcomes effectively. Basic knowledge of statistics is required to understand the concepts covered in this book. Also, some experience in R will be helpful.

What you will learn

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Naftali Jun 20, 2019

I enjoyed reading the book and learned a lot. Very practical with down-to-earth examples.

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Suarez Mar 01, 2023

I have been through the book, despite some challenges with code, it was a great experience learning it and useful for my work. Great.

alla Jul 30, 2019

I've spend a great part of my bachelor's and master's specializing in modeling and predicting time series.Same with my first years of work experience.Doing general data science work over the past several years I found my prior TS experience relevant and advantageous.To a similar extant, I am confident that this book can too provide a significant advantage to any data scientist.If you read it, experiment with the methods and get an intuition for the models and algorithms, you'll be a force multiplier to your organization.The book is simple, concise, quickly relates a method to its task, and leaves you with all the tools you need to perform industry grade time series modeling and prediction.Thanks and good luck,Lior.

Blaise F Egan Apr 02, 2021

Practical, hands-on guide to forecasting using R. Easy to use, as all the Packt books are.

Raquel Dourado Sep 13, 2019

This book is very didactic and it has valuable explanations.Organized and elegant.A must-read!

Hands-On Time Series Analysis with R: Perform time series analysis and forecasting using R

What do you get with Print?

Hands-On Time Series Analysis with R

Working with Date and Time Objects

Technical requirements

The date and time formats

Date and time objects in R

Creating a date or time index

Manipulation of date and time with the lubridate package

Reformatting date and time objects – the lubridate way

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Hands-On Time Series Analysis with R: Perform time series analysis and forecasting using R

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