Book Cover | Extracted summary |
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Book Title: Time Series Analysis and Its Applications With R Examples Author: Shumway, Robert H., Stoffer, David S. This book presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Theory and methodology are separated to allow presentations on different levels. | |
Book Title: Applied Time Series Analysis with R Author: Wayne A. Woodward, Henry L. Gray, Alan C. Elliott This book includes examples across a variety of fields, develops theory, and provides an R-based software package to aid in addressing time series problems in a broad spectrum of fields. | |
Book Title: Analyzing Financial Data and Implementing Financial Models Using R Author: Clifford Ang This book teaches students how to use R to analyze financial data and implement financial models from start (e.g., obtaining data) to finish (e.g., generating output expected for a particular analysis) using real-world data | |
Book Title: Practical Time Series Forecasting with R: A Hands-On Guide Author: Galit Shmueli and Kenneth C. Lichtendahl This book providea an applied approach to time-series forecasting which is an essential component of predictive analytics. This book also introduces popular forecasting methods and approaches used in a variety of business applications. | |
Book Title: Modeling Financial Time Series with S-PLUS® Author: Eric Zivot and Jiahui Wang This book represents an integration of theory, methods , and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics. This is the first book to show the power of S-PLUS for the analysis of time series data. | |
Book Title: Time Series Analysis With Applications in R Author: Jonathan D.Cryer and Kung-Sik Chan This book presents an accessible approach to understanding time series models and their applications. The new edition devotes two chapters to the frequency domain and three to time series regression models, models for heteroscedasticity, and threshold models. | |
Book Title: Statistics and Data Analysis for Financial Engineering Author: David Ruppert and David S. Matteson This book contains an ideal blend of innovative research and practical applications, tackles relevant investor problems, and provides a multi-disciplined approach, solving problems from both fundamental and non-traditional methods | |
Book Title: Financial Analytics with R Author: David Ruppert and David S. Matteson This book give examples using financial markets and economic data to illustrate important concepts. R Labs with real-data exercises give students practice in data analysis. | |
Book Title: R in Finance and Economics Author: Abhay Kumar Singh and David E Allen This book provides an introduction to the statistical software R and its application with an empirical approach in finance and economics. It is specifically targeted towards undergraduate and graduate students. It provides beginner-level introduction to R using RStudio and reproducible research examples. | |
Book Title: An Introduction to Analysis of Financial Data with R Author: Ruey S. Tsay This book explores basic concepts of visualization of financial data. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research. | |
Book Title: Statistical Analysis of Financial Data in R Author: René Carmona Although there are many books on mathematical finance, few deal with the statistical aspects of modern data analysis as applied to financial problems. This textbook fills this gap by addressing some of the most challenging issues facing financial engineers. It shows how modern statistical techniques can be used in the solutions of concrete financial problems. | |
Book Title: Multivariate Time Series Analysis Author: Ruey S. Tsay This book is the much anticipated sequel coming from one of the most influential and prominent experts on the topic of time series. Through a fundamental balance of theory and methodology, the book supplies readers with a comprehensible approach to financial econometric models and their applications to real-world empirical research. | |
Book Title: Computational Finance Author: Argimiro Arratia This book teaches you how to use the statistical tools and methods available in the free software R, for processing and analyzing real financial data | |
Book Title: Forecasting: principles and practice Author: Rob J Hyndman and George Athanasopoulos This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. |
rCharts
, I thought it would be interesting to document the timeline of this progression. For each step in the timeline, I will include a link to the source code (svn or github) of the package and a minimal example to demo the 'out-of-the-box' capability. In another iteration, I will explore more advanced usage of these functions. Separating the financial time series piece from graphing in general can get murky, and some of the timeline will differ from the timeline of R graphics and the timeline of R time series analysis.quantmod
getSymbols()
. The getSymbols()
function has been a work in progress since December 20, 2006. Fallout nuka world armor.plot.default
(As Old as Time Itself)ts
1999-08-27ts
package was added in R version 0.65.0 and significantly improved with release 1.5.0 in April 2002. There is a very good discussion of the improvements in Brian Ripley's 'Time Series in R 1.5.0' from Volume 2 of R News, June 2002. plot.ts()
added some nice features, such as the ability to plot multiple/wide time series, specify panels per series, and easily calculate acf, ARIMA,and HoltWinters.lattice
and grid
released with R 1.5.0 2002-04-29lattice
(Deepayan Sarkar) and gri
d (Paul Murrell) and also the improvements in ts
mentioned above., All of these are covered in Volume 2 of R News, June 2002. lattice
using grid
as its platform began an era of aesthetically pleasing and production-quality graphics straight from R. zoo
made it easier to work with irregular time series in R and 'bridged the gap.' plot.zoo()
allowed us plot.ts()
functionality for zoo objects.zoo
Meets lattice
2006-07-06zoo
adds a very handy xyplot.zoo()
function so there is no more need to convert zoo
objects before accessing all the power off lattice
.PerformanceAnalytics
chart.TimeSerie
s 2007-02-02PerformanceAnalytics
Bompani front loading washing machine bi2877 slvr illustrations free. addressed many of the graphical patterns necessary for financial performance reporting. chart.TimeSeries()
and chart.BarVaR()
serve as the base for functions such as the very useful charts.PerformanceSummary()
below. In addition to the charts, PerformanceAnalytics
adds many useful tables and makes both easy and very complicated performance calculations accessible in R. Most of the PerformanceAnalytics
functions require a xts
return series rather than price.ggplot2
2007-06-10ggplot
was significant, but the 2007 rewrite into ggplot2
0.5 completely changed R graphics. Although ggplot2
is comprehensive and not designed specifically for time series plotting, I include it in the timeline due to both its significant impact on R graphics and its ability to handle dates/times on the x-axis. To use xts with ggplot2
, a simple conversion to a wide or long format data.frame is necessary.quantmod
/TTR
chartSeries
2007-10-07quantmod
and TTR
were designed to give R technical analysis tools and calculations. The chartSeries()
function makes OHLC, candlesticks, and bars charts of prices easy. Adding technical analysis, such as Bollinger Bands, RSI, MACD, becomes a couple letter function.xts
plot.xts
2008-02-17xts
. I strongly recommend reading the xts
vignette to understand the benefits of xts
. It is now the standard for financial time series in R. xts
ported plot.zoo()
to its own plot()
method. A xyplot.xts()
was also provided for use with lattice
.timeSeries
plot
2009-05-17timeSeries
plot()
method is basically a port of R's plot.ts()
. It does not significantly add any plotting functionality, but I include it for completeness and since the Rmetrics team offers robust financial analysis through its many R packages that depend on the timeSeries
object.googleVis
2010-11-30 and sourcegoogleVis
adds accessible interactivity to charts using Google Charts. There are a variety of chart types along with very nice animation/storyboard options.xtsExtra
by Michael Weylandt sought to improve the xts
plotting methods as described well in Michael's announcement to R-Sig-Finance.zoo
meets ggplot2
with autoplot.zoo()
2012-10-14zoo
meets lattice
above, autoplot.zoo()
makes plotting zoo
with ggplot2
much easier. As the codes shows below, xts
can also use autoplot.zoo()
with no explicit transformation.rCharts
2013rCharts
released in 2013 by Ramnath Vaidyanathan makes interactive charts straight from R with built-in functionality from frameworks built on top of d3.js
, raphael
, and other leading javascript libraries. This interactivity offers a whole new level of discovery and exploration previously not available with static graphics. See the examples below. The examples are only minimal examples to demonstrate how much can be done in a few lines of code. For more thorough demos, check out the gallery.