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Hands-On Data Science with R

You're reading from   Hands-On Data Science with R Techniques to perform data manipulation and mining to build smart analytical models using R

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789139402
Length 420 pages
Edition 1st Edition
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Authors (4):
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Nataraj Dasgupta Nataraj Dasgupta
Author Profile Icon Nataraj Dasgupta
Nataraj Dasgupta
Vitor Bianchi Lanzetta Vitor Bianchi Lanzetta
Author Profile Icon Vitor Bianchi Lanzetta
Vitor Bianchi Lanzetta
Doug Ortiz Doug Ortiz
Author Profile Icon Doug Ortiz
Doug Ortiz
Ricardo Anjoleto Farias Ricardo Anjoleto Farias
Author Profile Icon Ricardo Anjoleto Farias
Ricardo Anjoleto Farias
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Table of Contents (16) Chapters Close

Preface 1. Getting Started with Data Science and R FREE CHAPTER 2. Descriptive and Inferential Statistics 3. Data Wrangling with R 4. KDD, Data Mining, and Text Mining 5. Data Analysis with R 6. Machine Learning with R 7. Forecasting and ML App with R 8. Neural Networks and Deep Learning 9. Markovian in R 10. Visualizing Data 11. Going to Production with R 12. Large Scale Data Analytics with Hadoop 13. R on Cloud 14. The Road Ahead 15. Other Books You May Enjoy

Programming an HMM with R

The HMM can be used in the finance field for a great number of things. Features such as regime identification, volatility clustering, and anti-correlation (return and volatility) can all be extracted from financial data through using the HMM. R has several great packages to deal with the HMM. This chapter uses one called ldhmm, which implements the homogeneous first-order HMM.

This link will guide you through the package documentation: https://cran.r-project.org/web/packages/ldhmm/ldhmm.pdf. The word first-order means that the state in time t is only influenced by the state in time t-1. Make sure that you have ldhmm installed by using the following code:

if(!require('ldhmm')){install.packages('ldhmm')}

Let's begin with data. The ldhmm package comes with daily data from the Standard & Poor 500 Index (SPX). Run the following...

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