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R for Data Science Cookbook (n)

You're reading from   R for Data Science Cookbook (n) Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques

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Product type Paperback
Published in Jul 2016
Publisher
ISBN-13 9781784390815
Length 452 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Author Profile Icon Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu)
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Toc

Table of Contents (14) Chapters Close

Preface 1. Functions in R FREE CHAPTER 2. Data Extracting, Transforming, and Loading 3. Data Preprocessing and Preparation 4. Data Manipulation 5. Visualizing Data with ggplot2 6. Making Interactive Reports 7. Simulation from Probability Distributions 8. Statistical Inference in R 9. Rule and Pattern Mining with R 10. Time Series Mining with R 11. Supervised Machine Learning 12. Unsupervised Machine Learning Index

Introduction

The first example of time series analysis in human history occurred in ancient Egypt. The ancient Egyptians recorded the inundation (rise) and relinquishment (fall) of the Nile river every day, noting when fertile silt and moisture occurred. Based on these records, they found that the inundation period began when the sun rose at the same time as the Sirius star system became visible. By being able to predict the inundation period, the ancient Egyptians were able to make sophisticated agricultural decisions, greatly improving the yield of their farming activities.

As demonstrated by the ancient Egyptian inundation period example, time series analysis is a method that can extract patterns or meaningful statistics from data with temporal information. It allows us to forecast future values based on observed results. One can apply time series analysis to any data that has temporal information. For example, an economist can perform time series analysis to predict the GDP growth rate...

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