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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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N/A Nov 06, 2024

November 5, 2024 Dear Packt Customer Service, Re : October 16, 2024 email Please advise me if a copy of R. Krispin's book, "Hands On Time Series Analysis" is available to purchase, as I never received a copy of this book. Thank you for your consideration. Sincerely, Pamela L. Van Horn (email: plvh2015@outlook.com)

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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.

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Eliab Luvanda Oct 10, 2021

Good book. Reader friendly for a novice, brief, and well organized. It is a good introduction to time series analysis and forecasting using R.

Big T Jun 04, 2021

This is a good book, but ther are a number of mistakes that the editor should have caught. For example on page 184, the code should show hour=9 which leads to a different graph. Need better quality checking

Amazon Customer May 03, 2021

Can't fault the contents of the book, however, the graphs in the print are all black and white which makes it difficult to read them. And, the graphs were clearly meant to be in colour as the text refers to red and green markings which you cannot see. Makes me think the book is counterfeit but I missed my window to return.

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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