Visualizing Time Series Data in R
Learn how to visualize time series in R, then practice with a stock-picking case study.
Start Course for Free4 Hours11 Videos45 Exercises12,963 Learners3550 XPQuantitative Analyst with R TrackTime Series with R Track
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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.
- 1
R Time Series Visualization Tools
FreeThis chapter will introduce you to basic R time series visualization tools.
Refresher on xts and the plot() function50 xpplot() function quiz50 xpplot() function - basic parameters100 xpplot() function - basic parameters (2)100 xpControl graphic parameters100 xpGraphic parameters quiz50 xpOther useful visualizing functions50 xpAdding an extra series to an existing chart100 xpHighlighting events in a time series100 xpHighlighting a specific period in a time series100 xpA fancy stock chart100 xpA fancy stock chart (2)100 xp - 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.
Univariate time series analysis50 xpRepresenting a univariate time series100 xpOther visualization tools50 xpHistogram of returns100 xpBox and whisker plot100 xpAutocorrelation100 xpq-q plot100 xpHow to use everything we learned so far?50 xpA comprehensive time series diagnostic100 xpA comprehensive time series diagnostic (2)100 xpTime Series quiz50 xpStock quiz50 xp - 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.
Dealing with higher dimensions50 xpTwo time series grouped or stacked100 xpVisualizing bivariate relationships100 xpMultivariate time series50 xpCorrelation matrix100 xpScatterplots for multiple pairs of data100 xpCorrelation plot100 xpHigher dimension time series50 xpCorrelation matrix as heatmap100 xpWrap up quiz50 xp - 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.
Case study presentation50 xpCurrent portfolio description100 xpExisting portfolio quiz50 xpNew stocks50 xpNew stocks description100 xpNew stocks description (2)100 xpPortfolio quiz50 xpCompare old and new portfolios100 xpA more accurate comparison of portfolios100 xpWrap up quiz50 xpCongratulations!50 xp
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 portfolioCollaborators
Prerequisites
Manipulating Time Series Data with xts and zoo in R
Arnaud Amsellem
Quantitative Trader and creator of the R Trader blog
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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