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Practical Data Analysis

You're reading from   Practical Data Analysis For small businesses, analyzing the information contained in their data using open source technology could be game-changing. All you need is some basic programming and mathematical skills to do just that.

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Product type Paperback
Published in Oct 2013
Publisher Packt
ISBN-13 9781783280995
Length 360 pages
Edition 1st Edition
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Author (1):
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Hector Cuesta Hector Cuesta
Author Profile Icon Hector Cuesta
Hector Cuesta
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Table of Contents (24) Chapters Close

Practical Data Analysis
Credits
Foreword
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started FREE CHAPTER 2. Working with Data 3. Data Visualization 4. Text Classification 5. Similarity-based Image Retrieval 6. Simulation of Stock Prices 7. Predicting Gold Prices 8. Working with Support Vector Machines 9. Modeling Infectious Disease with Cellular Automata 10. Working with Social Graphs 11. Sentiment Analysis of Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with IPython and Wakari Setting Up the Infrastructure Index

Chapter 7. Predicting Gold Prices

In this chapter, you will be introduced to the basic concepts of time series data and regression. First, we distinguish some of the basic concepts such as trend, seasonality, and noise. Then we introduce the historic gold prices time series and also get an overview on how to perform a forecast using kernel ridge regression. Later, we present a regression using the smoothed time series as an input.

This chapter will cover:

  • Working with the time series data

  • The data – historical gold prices

  • Nonlinear regression

  • Kernel ridge regression

  • Smoothing the gold prices time series

  • Predicting in the smoothed time series

  • Contrasting the predicted value

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