Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Simulation for Data Science with R

You're reading from   Simulation for Data Science with R Effective Data-driven Decision Making

Arrow left icon
Product type Paperback
Published in Jun 2016
Publisher Packt
ISBN-13 9781785881169
Length 398 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Matthias Templ Matthias Templ
Author Profile Icon Matthias Templ
Matthias Templ
Arrow right icon
View More author details
Toc

Table of Contents (13) Chapters Close

Preface 1. Introduction 2. R and High-Performance Computing FREE CHAPTER 3. The Discrepancy between Pencil-Driven Theory and Data-Driven Computational Solutions 4. Simulation of Random Numbers 5. Monte Carlo Methods for Optimization Problems 6. Probability Theory Shown by Simulation 7. Resampling Methods 8. Applications of Resampling Methods and Monte Carlo Tests 9. The EM Algorithm 10. Simulation with Complex Data 11. System Dynamics and Agent-Based Models Index

Bootstrapping in time series


Two methods are often used in bootstrapping of time series:

  • To estimate a model and draw from the residuals (see second last section on bootstrapping regression models by bootstrapping residuals)

  • Moving blocks bootstrap methods

We concentrate in the following, on the moving blocks bootstrap. It is a method that is often applied and mentioned in literature, but with limited success. To show the limitations of this approach is one goal of this section.

The idea is to divide the data in blocks and to sample with replacement within blocks. This allows us to not completely ignore the relationship between the observations. Relationships between observations are typically present in time series. For example, the next value will depend on the previous value. Think also on the trend, seasonality, and periodicity.

In principle, the time series can be divided in non-overlapping or overlapping blocks.

We will show an overlapping moving blocks bootstrap for estimating the autocorrelation...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime