Introductory Time Series With R

Author: Paul S.P. Cowpertwait
Publisher: Springer Science & Business Media
ISBN: 0387886982
Size: 29.50 MB
Format: PDF, Kindle
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This book gives you a step-by-step introduction to analysing time series using the open source software R. Each time series model is motivated with practical applications, and is defined in mathematical notation.

Time Series Analysis And Its Applications

Author: Robert H. Shumway
Publisher: Springer
ISBN: 144197864X
Size: 44.60 MB
Format: PDF, ePub, Docs
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Also new to this edition is the enhanced use of the freeware statistical package R. In particular, R code is now included in the text for nearly all of the numerical examples.

R Data Visualization Cookbook

Author: Atmajitsinh Gohil
Publisher: Packt Publishing Ltd
ISBN: 1783989513
Size: 75.54 MB
Format: PDF, Kindle
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It is not possible to cover the topic of time series analysis in detail and I would
highly recommend readers to refer to Introductory Time Series with R given in the
See also section. In this recipe, we will explore a simple additive decomposition
model. The model is represented as follows: Xt = Mt+St+Zt In this notation, the
variables at time t are as follows: f Xt: This is an observed series f Mt: This is the
trend f St: This is the seasonal effect f Zt: This is the error term It is also possible to
use a ...

An Introduction To Analysis Of Financial Data With R

Author: Ruey S. Tsay
Publisher: John Wiley & Sons
ISBN: 1119013461
Size: 62.55 MB
Format: PDF
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The book is also an excellent resource for researchers and practitioners in the fields of business, finance, and economics who would like to enhance their understanding of financial data and today's financial markets.

Modeling Techniques In Predictive Analytics With Python And R

Author: Thomas W. Miller
Publisher: FT Press
ISBN: 013389214X
Size: 14.21 MB
Format: PDF, Docs
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Introductory Time Series with R. New York: Springer. Cox, D. R. 1958. Planning
of Experiments. New York: Wiley. Cox, D. R. 1970. Analysis of Binary Data.
London: Chapman and Hall. Cox, T. F. and M. A. A. Cox 1994. Multidimensional
Scaling. London: Chapman & Hall. 296 Craddock, J. (ed.) 2012. VideoHound's
Golden Movie Retriever 2013: The Complete Guide to Movies on All Home
Entertainment Formats. Farmington Hills, Mich.: Gale. Cranor, L. F. and B. A.
LaMacchia 1998.

Modeling Techniques In Predictive Analytics

Author: Thomas W. Miller
Publisher: FT Press
ISBN: 0133886190
Size: 43.32 MB
Format: PDF, ePub, Docs
View: 5842
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Introductory Time Series with R. New York: Springer. Cox, D. R. 1958. Planning
of Experiments. New York: Wiley. Cox, D. R. 1970. Analysis of Binary Data.
London: Chapman and Hall. Cox, T. F. and M. A. A. Cox 1994. Multidimensional
Scaling. London: Chapman & Hall. 244 Craddock, J. (ed.) 2012. VideoHound's
Golden Movie Retriever 2013: The Complete Guide to Movies on All Home
Entertainment Formats. Farmington Hills, Mich.: Gale. Cranor, L. F. and B. A.
LaMacchia 1998.

Mastering R For Quantitative Finance

Author: Edina Berlinger
Publisher: Packt Publishing Ltd
ISBN: 1783552085
Size: 65.95 MB
Format: PDF, ePub, Mobi
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Meanwhile, we have provided a useful introduction to some tips and tricks to start
modeling with R for quantitative and empirical finance. We hope that you find
these exercises useful, but again, it should be noted that this chapter is far from
being complete both from time series and econometric theory, and from R
programming's point of view. The R programming language is very well
documented on the Internet, and the R user's community consists of thousands of
advanced and ...

Wavelet Methods In Statistics With R

Author: Guy Nason
Publisher: Springer Science & Business Media
ISBN: 0387759611
Size: 14.77 MB
Format: PDF
View: 6096
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5 Multiscale Time Series Analysis 5.1 Introduction The modelling and analysis of
dependent phenomena are among the most important and challenging areas of
statistics. Put simply, a time series is a set of observations with some definite
ordering in time. For example, we may denote an observed (discrete) time series
of length n by x\, . . . , xn. The main difference between time series data and '
ordinary' data is that time series observations are not independent but typically
possess a ...

Time Series Analysis For The Social Sciences

Author: Janet M. Box-Steffensmeier
Publisher: Cambridge University Press
ISBN: 0521871166
Size: 80.82 MB
Format: PDF, Kindle
View: 2427
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The incorporation of time series analysis into standard statistical packages such
as STATA and R, as well as the existence of specialized packages such as RATS
and Eviews, provides an additional motivation because it enables ... We have
found over our years of teaching time series that, although many social science
students have the brain power to learn time series methods, they often lack the
training and motivation to use the most well-known books on the topic. We
specifically ...