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  • Publicado : 2 de noviembre de 2010
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Time Series Analysis with R Part I

Walter Zucchini, Oleg Nenadi´ c

Contents
1 Getting started 1.1 Downloading and Installing R . . . . . . . . . . . . . . . . . . . . 1.2 Data Preparation andImport in R . . . . . . . . . . . . . . . . . 1.3 Basic R–commands: Data Manipulation and Visualization . . . . 2 Simple Component Analysis 2.1 Linear Filtering of Time Series . . . . . . . . . . . .. . . . . . . . 2.2 Decomposition of Time Series . . . . . . . . . . . . . . . . . . . . 2.3 Regression analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Exponential Smoothing 3.1Introductionary Remarks . . . . . . . . . . . . . . . . . . . . . . . 3.2 Exponential Smoothing and Prediction of Time Series . . . . . . . 4 ARIMA–Models 4.1 Introductionary Remarks . . . . . . . . . . . .. . . . . . 4.2 Analysis of Autocorrelations and Partial Autocorrelations 4.3 Parameter–Estimation of ARIMA–Models . . . . . . . . 4.4 Diagnostic Checking . . . . . . . . . . . . . . . . . . . . 4.5Prediction of ARIMA–Models . . . . . . . . . . . . . . . A Function Reference . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 2 3 8 8 9 11 14 14 14 17 17 17 18 19 20 22

1

Chapter 1Getting started
1.1 Downloading and Installing R

R is a widely used environment for statistical analysis. The striking difference between R and most other statistical software is that it is free softwareand that it is maintained by scientists for scientists. Since its introduction in 1996, the R–project has gained many users and contributors, which continously extend the capabilities of R byreleasing add–ons (packages) that offer previously not available functions and methods or improve the existing ones. One disadvantage or advantage, depending on the point of view, is that R is used within acommand–line interface, which imposes a slightly steeper learning curve than other software. But, once this burden hab been taken, R offers almost unlimited possibilities for statistical data...
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