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# Visualizing Time Series Data in R

Learn how to visualize time series in R, then practice with a stock-picking case study.

4 hours11 videos45 exercises

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## Course Description

As the saying goes, “A chart is worth a thousand words”. This is why visualization is the most used and powerful way to get a better understanding of your data. After this course you will have a very good overview of R time series visualisation capabilities and you will be able to better decide which model to choose for subsequent analysis. You will be able to also convey the message you want to deliver in an efficient and beautiful way.

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1. 1

### R Time Series Visualization Tools

Free

This chapter will introduce you to basic R time series visualization tools.

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Refresher on xts and the plot() function
50 xp
plot() function quiz
50 xp
plot() function - basic parameters
100 xp
plot() function - basic parameters (2)
100 xp
Control graphic parameters
100 xp
Graphic parameters quiz
50 xp
Other useful visualizing functions
50 xp
Adding an extra series to an existing chart
100 xp
Highlighting events in a time series
100 xp
Highlighting a specific period in a time series
100 xp
A fancy stock chart
100 xp
A fancy stock chart (2)
100 xp
2. 2

### Univariate Time Series

Univariate plots are designed to learn as much as possible about the distribution, central tendency and spread of the data at hand. In this chapter you will be presented with some visual tools used to diagnose univariate times series.

3. 3

### Multivariate Time Series

What to do if you have to deal with multivariate time series? In this chapter, you will learn how to identify patterns in the distribution, central tendency and spread over pairs or groups of data.

4. 4

### Case study: Visually Selecting a Stock That Improves Your Existing Portfolio

Let's put everything you learned so far in practice! Imagine you already own a portfolio of stocks and you have some spare cash to invest, how can you wisely select a new stock to invest your additional cash? Analyzing the statistical properties of individual stocks vs. an existing portfolio is a good way of approaching the problem.

### In the following Tracks

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#### Time Series with R

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datasets

Returns for XOM, C, MSFT, DOW, and YHOOExisting portfolioStock data for GS, KO, DIS, and CATDaily stocks for YHOO, MSFT, C, and DOWDaily returns for AppleOld versus new portfolio

collaborators

Arnaud Amsellem

Arnaud has over 20 years of experience as a quantitative trader. He is creator and author of the R Trader blog (www.thertrader.com).
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