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Hands-On Time Series Analysis with R

You're reading from Hands-On Time Series Analysis with R Perform time series analysis and forecasting using R

Product type Paperback

Published in May 2019

Publisher Packt

ISBN-13 9781788629157

Length 448 pages

Edition 1st Edition

Languages

R

Tools

Plotly

Concepts

Data Analysis

Author (1):

Rami Krispin

View More author details

Table of Contents (14) Chapters

Preface

1. Introduction to Time Series Analysis and R FREE CHAPTER

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

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Date and time objects in R

The base package, one of R's core packages, provides two types of date and time classes:

Date: This is a simple representation of a calendar date following the ISO 8601 international standard format (or the Gregorian calendar format) using the YYYY-m-d date format. Each date object has a numeric value of the number of days since the origin point (the default setting is 1970-01-01). In the Handling numeric date objects section in this chapter, we will discuss the usage of the origin in the reformatting process of date objects in more detail. It will make sense to use this format when the frequency of the data is daily or lower (for example, monthly, quarterly, and so on) and the time of the day doesn't matter.
POSIXct/POSIXlt: Also known as the DateTime classes (that is, they represent both date and time), these are two POSIX date/time classes...

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Authors (1)

Rami Krispin

Rami Krispin

Rami Krispin is a data scientist at a major Silicon Valley company, where he focuses on time series analysis and forecasting. In his free time, he also develops open source tools and is the author of several R packages, including the TSstudio package for time series analysis and forecasting applications. Rami holds an MA in Applied Economics and an MS in actuarial mathematics from the University of MichiganAnn Arbor.

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