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Basic data analysis for time series with R /
"This book emphasizes the collaborative analysis of data that is used to collect increments of time or space. Written at a readily accessible level, but with the necessary theory in mind, the author uses frequency- and time-domain and trigonometric regression as themes throughout the book. The...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley & Sons, Inc.,
[2014]
|
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
MARC
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|---|---|---|---|
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| 100 | 1 | |a Derryberry, DeWayne R., |e author. | |
| 245 | 1 | 0 | |a Basic data analysis for time series with R / |c DeWayne R. Derryberry, Department of Mathematics and Statistics, Idaho State University, Voise, ID. |
| 264 | 1 | |a Hoboken, New Jersey : |b John Wiley & Sons, Inc., |c [2014] | |
| 264 | 4 | |c ©2014 | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file | ||
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "This book emphasizes the collaborative analysis of data that is used to collect increments of time or space. Written at a readily accessible level, but with the necessary theory in mind, the author uses frequency- and time-domain and trigonometric regression as themes throughout the book. The content includes modern topics such as wavelets, Fourier series, and Akaike's Information Criterion (AIC), which is not typical of current-day "classics." Applications to a variety of scientific fields are showcased. Exercise sets are well crafted with the express intent of supporting pedagogy through recognition and repetition. R subroutines are employed as the software and graphics tool of choice. Brevity is a key component to the retention of the subject matter. The book presumes knowledge of linear algebra, probability, data analysis, and basic computer programming"-- |c Provided by publisher | ||
| 520 | |a "This book emphasizes the collaborative analysis of data that is used to collect increments of time or space. Written at a readily accessible level, but with the necessary theory in mind, the author uses frequency- and time-domain and trigonometric regression as themes throughout the book"-- |c Provided by publisher | ||
| 588 | 0 | |a Print version record and CIP data provided by publisher. | |
| 505 | 0 | 0 | |g Machine generated contents note: |g 1. |t R Basics -- |g 1.1. |t Getting Started, -- |g 1.2. |t Special R Conventions, -- |g 1.3. |t Common Structures, -- |g 1.4. |t Common Functions, -- |g 1.5. |t Time Series Functions, -- |g 1.6. |t Importing Data, -- |t Exercises, -- |g 2. |t Review of Regression and More About R -- |g 2.1. |t Goals of this Chapter, -- |g 2.2. |t The Simple(ST) Regression Model, -- |g 2.2.1. |t Ordinary Least Squares, -- |g 2.2.2. |t Properties of OLS Estimates, -- |g 2.2.3. |t Matrix Representation of the Problem, -- |g 2.3. |t Simulating the Data from a Model and Estimating the Model Parameters in R, -- |g 2.3.1. |t Simulating Data, -- |g 2.3.2. |t Estimating the Model Parameters in R, -- |g 2.4. |t Basic Inference for the Model, -- |g 2.5. |t Residuals Analysis[2014]What Can Go Wrong, -- |g 2.6. |t Matrix Manipulation in R, -- |g 2.6.1. |t Introduction, -- |g 2.6.2. |t OLS the Hard Way, -- |g 2.6.3. |t Some Other Matrix Commands, -- |t Exercises, -- |g 3. |t The Modeling Approach Taken in this Book and Some Examples of Typical Serially Correlated Data -- |g 3.1. |t Signal and Noise, -- |g 3.2. |t Time Series Data, -- |g 3.3. |t Simple Regression in the Framework, -- |g 3.4. |t Real Data and Simulated Data, -- |g 3.5. |t The Diversity of Time Series Data, -- |g 3.6. |t Getting Data Into R, -- |g 3.6.1. |t Overview, -- |g 3.6.2. |t The Diskette and the scan() and ts() Functions[2014]New York City Temperatures, -- |g 3.6.3. |t The Diskette and the read.table() Function[2014]The Semmelweis Data, -- |g 3.6.4. |t Cut and Paste Data to a Text Editor, -- |t Exercises, -- |g 4. |t Some Comments on Assumptions -- |g 4.1. |t Introduction, -- |g 4.2. |t The Normality Assumption, -- |g 4.2.1. |t Right Skew, -- |g 4.2.2. |t Left Skew, -- |g 4.2.3. |t Heavy Tails, -- |g 4.3. |t Equal Variance, -- |g 4.3.1. |t Two-Sample t-Test, -- |g 4.3.2. |t Regression, -- |g 4.4. |t Independence, -- |g 4.5. |t Power of Logarithmic Transformations Illustrated, -- |g 4.6. |t Summary, -- |t Exercises, -- |g 5. |t The Autocorrelation Function And AR(1), AR(2) Models -- |g 5.1. |t Standard Models[2014]What are the Alternatives to White Noise?, -- |g 5.2. |t Autocovariance and Autocorrelation, -- |g 5.2.1. |t Stationarity, -- |g 5.2.2. |t A Note About Conditions, -- |g 5.2.3. |t Properties of Autocovariance, -- |g 5.2.4. |t White Noise, -- |g 5.2.5. |t Estimation of the Autocovariance and Autocorrelation, -- |g 5.3. |t The acf() Function in R, -- |g 5.3.1. |t Background, -- |g 5.3.2. |t The Basic Code for Estimating the Autocovariance, -- |g 5.4. |t The First Alternative to White Noise: Autoregressive Errors[2014]AR(1), AR(2), -- |g 5.4.1. |t Definition of the AR(1) and AR(2) Models, -- |g 5.4.2. |t Some Preliminary Facts, -- |g 5.4.3. |t The AR(1) Model Autocorrelation and Autocovariance, -- |g 5.4.4. |t Using Correlation and Scatterplots to Illustrate the AR(1) Model, -- |g 5.4.5. |t The AR(2) Model Autocorrelation and Autocovariance, -- |g 5.4.6. |t Simulating Data for AR(m) Models, -- |g 5.4.7. |t Examples of Stable and Unstable AR(1) Models, -- |g 5.4.8. |t Examples of Stable and Unstable AR(2) Models, -- |t Exercises, -- |g 6. |t The Moving Average Models MA(1) And MA(2) -- |g 6.1. |t The Moving Average Model, -- |g 6.2. |t The Autocorrelation for MA(1) Models, -- |g 6.3. |t A Duality Between MA(l) And AR(m) Models, -- |g 6.4. |t The Autocorrelation for MA(2) Models, -- |g 6.5. |t Simulated Examples of the MA(1) Model, -- |g 6.6. |t Simulated Examples of the MA(2) Model, -- |g 6.7. |t AR(m) and MA(l) model acf() Plots, -- |t Exercises, -- |g 7. |t Review of Transcendental Functions and Complex Numbers -- |g 7.1. |t Background, -- |g 7.2. |t Complex Arithmetic, -- |g 7.2.1. |t The Number i, -- |g 7.2.2. |t Complex Conjugates, -- |g 7.2.3. |t The Magnitude of a Complex Number, -- |g 7.3. |t Some Important Series, -- |g 7.3.1. |t The Geometric and Some Transcendental Series, -- |g 7.3.2. |t A Rationale for Euler's Formula, -- |g 7.4. |t Useful Facts About Periodic Transcendental Functions, -- |t Exercises, -- |g 8. |t The Power Spectrum and the Periodogram -- |g 8.1. |t Introduction, -- |g 8.2. |t A Definition and a Simplified Form for p(f), -- |g 8.3. |t Inverting p(f) to Recover the Ck Values, -- |g 8.4. |t The Power Spectrum for Some Familiar Models, -- |g 8.4.1. |t White Noise, -- |g 8.4.2. |t The Spectrum for AR(1) Models, -- |g 8.4.3. |t The Spectrum for AR(2) Models, -- |g 8.5. |t The Periodogram, a Closer Look, -- |g 8:5.1. |t Why is the Periodogram Useful?, -- |g 8.5.2. |t Some Naive Code for a Periodogram, -- |g 8.5.3. |t An Example[2014]The Sunspot Data, -- |g 8.6. |t The Function spec.pgram() in R, -- |t Exercises, -- |g 9. |t Smoothers, The Bias-Variance Tradeoff, and the Smoothed Periodogram -- |g 9.1. |t Why is Smoothing Required?, -- |g 9.2. |t Smoothing, Bias, and Variance, -- |g 9.3. |t Smoothers Used in R, -- |g 9.3.1. |t The R Function lowess(), -- |g 9.3.2. |t The R Function smooth.spline(), -- |g 9.3.3. |t Kernel Smoothers in spec.pgram(), -- |g 9.4. |t Smoothing the Periodogram for a Series With a Known and Unknown Period, -- |g 9.4.1. |t Period Known, -- |g 9.4.2. |t Period Unknown, -- |g 9.5. |t Summary, -- |t Exercises, -- |g 10. |t A Regression Model for Periodic Data -- |g 10.1. |t The Model, |
| 505 | 0 | 0 | |g 10.2. |t An Example: The NYC Temperature Data, -- |g 10.2.1. |t Fitting a Periodic Function, -- |g 10.2.2. |t An Outlier, -- |g 10.2.3. |t Refitting the Model with the Outlier Corrected, -- |g 10.3. |t Complications 1: CO2 Data, -- |g 10.4. |t Complications 2: Sunspot Numbers, -- |g 10.5. |t Complications 3: Accidental Deaths, -- |g 10.6. |t Summary, -- |t Exercises, -- |g 11. |t Model Selection and Cross-Validation -- |g 11.1. |t Background, -- |g 11.2. |t Hypothesis Tests in Simple Regression, -- |g 11.3. |t A More General Setting for Likelihood Ratio Tests, -- |g 11.4. |t A Subtlety Different Situation, -- |g 11.5. |t Information Criteria, -- |g 11.6. |t Cross-validation (Data Splitting): NYC Temperatures, -- |g 11.6.1. |t Explained Variation, R2, -- |g 11.6.2. |t Data Splitting, -- |g 11.6.3. |t Leave-One-Out Cross-Validation, -- |g 11.6.4. |t AIC as Leave-One-Out Cross-Validation, -- |g 11.7. |t Summary, -- |t Exercises, -- |g 12. |t Fitting Fourier series -- |g 12.1. |t Introduction: More Complex Periodic Models, -- |g 12.2. |t More Complex Periodic Behavior: Accidental Deaths, -- |g 12.2.1. |t Fourier Series Structure, -- |g 12.2.2. |t R Code for Fitting Large Fourier Series, -- |g 12.2.3. |t Model Selection with AIC, -- |g 12.2.4. |t Model Selection with Likelihood Ratio Tests, -- |g 12.2.5. |t Data Splitting, -- |g 12.2.6. |t Accidental Deaths[2014]Some Comment on Periodic Data, -- |g 12.3. |t The Boise River Flow data, -- |g 12.3.1. |t The Data, -- |g 12.3.2. |t Model Selection with AIC, -- |g 12.3.3. |t Data Splitting, -- |g 12.3.4. |t The Residuals, -- |g 12.4. |t Where Do We Go from Here?, -- |t Exercises, -- |g 13. |t Adjusting for AR(1) Correlation in Complex Models -- |g 13.1. |t Introduction, -- |g 13.2. |t The Two-Sample t-Test[2014]UNCUT and Patch-Cut Forest, -- |g 13.2.1. |t The Sleuth Data and the Question of Interest, -- |g 13.2.2. |t A Simple Adjustment for t-Tests When the Residuals Are AR(1), -- |g 13.2.3. |t A Simulation Example, -- |g 13.2.4. |t Analysis of the Sleuth Data, -- |g 13.3. |t The Second Sleuth Case[2014]Global Warming, A Simple Regression, -- |g 13.3.1. |t The Data and the Question, -- |g 13.3.2. |t Filtering to Produce (Quasi- )Independent Observations, -- |g 13.3.3. |t Simulated Example[2014]Regression, -- |g 13.3.4. |t Analysis of the Regression Case, -- |g 13.3.5. |t The Filtering Approach for the Logging Case, -- |g 13.3.6. |t A Few Comments on Filtering, -- |g 13.4. |t The Semmelweis Intervention, -- |g 13.4.1. |t The Data, -- |g 13.4.2. |t Why Serial Correlation?, -- |g 13.4.3. |t How This Data Differs from the Patch/Uncut Case, -- |g 13.4.4. |t Filtered Analysis, -- |g 13.4.5. |t Transformations and Inference, -- |g 13.5. |t The NYC Temperatures (Adjusted), -- |g 13.5.1. |t The Data and Prediction Intervals, -- |g 13.5.2. |t The AR(1) Prediction Model, -- |g 13.5.3. |t A Simulation to Evaluate These Formulas, -- |g 13.5.4. |t Application to NYC Data, -- |g 13.6. |t The Boise River Flow Data: Model Selection With Filtering, -- |g 13.6.1. |t The Revised Model Selection Problem, -- |g 13.6.2. |t Comments on R2 and R2pred' -- |g 13.6.3. |t Model Selection After Filtering with a Matrix, -- |g 13.7. |t Implications of AR(1) Adjustments and the "Skip" Method, -- |g 13.7.1. |t Adjustments for AR(1) Autocorrelation, -- |g 13.7.2. |t Impact of Serial Correlation on p-Values, -- |g 13.7.3. |t The "skip" Method, -- |g 13.8. |t Summary, -- |t Exercises, -- |g 14. |t The Backshift Operator, the Impulse Response Function, and General ARMA Models -- |g 14.1. |t The General ARMA Model, -- |g 14.1.1. |t The Mathematical Formulation, -- |g 14.1.2. |t The arima.sim() Function in R Revisited, -- |g 14.1.3. |t Examples of ARMA(m, l) Models, -- |g 14.2. |t The Backshift (Shift, Lag) Operator, -- |g 14.2.1. |t Definition of B, -- |g 14.2.2. |t The Stationary Conditions for a General AR(m) Model, -- |g 14.2.3. |t ARMA(m, l) Models and the Backshift Operator, -- |g 14.2.4. |t More Examples of ARMA(m, l) Models, -- |g 14.3. |t The Impulse Response Operator[2014]Intuition, -- |g 14.4. |t Impulse Response Operator, g(B)[2014]Computation, -- |g 14.4.1. |t Definition of g(B), -- |g 14.4.2. |t Computing the Coefficients, -- |g 14.4.3. |t Plotting an Impulse Response Function, -- |g 14.5. |t Interpretation and Utility of the Impulse Response Function, -- |t Exercises, -- |g 15. |t The Yule[2014]Walker Equations and the Partial Autocorrelation Function -- |g 15.1. |t Background, -- |g 15.2. |t Autocovariance of an ARMA(m, /) Model, -- |g 15.2.1. |t A Preliminary Result, -- |g 15.2.2. |t The Autocovariance Function for ARMA(m, /) Models, -- |g 15.3. |t AR(m) and the Yule[2014]Walker Equations, -- |g 15.3.1. |t The Equations, -- |g 15.3.2. |t The R Function aryw() with an AR(3) Example, -- |g 15.3.3. |t Information Criteria-Based Model Selection Using aryw(), -- |g 15.4. |t The Partial Autocorrelation Plot, -- |g 15.4.1. |t A Sequence of Hypothesis Tests, -- |g 15.4.2. |t The pacf() Function[2014]Hypothesis Tests Presented in a Plot, -- |g 15.5. |t The Spectrum For Arma Processes, -- |g 15.6. |t Summary, -- |t Exercises, -- |g 16. |t Modeling Philosophy and Complete Examples -- |g 16.1. |t Modeling Overview, -- |g 16.1.1. |t The Algorithm, |
| 505 | 0 | 0 | |g Note continued: |g 16.1.2. |t The Underlying Assumption, -- |g 16.1.3. |t An Example Using an AR(m) Filter to Model MA(3), -- |g 16.1.4. |t Generalizing the "Skip" Method, -- |g 16.2. |t A Complex Periodic Model[2014]Monthly River Flows, Fumas 1931-1978, -- |g 16.2.1. |t The Data, -- |g 16.2.2. |t A Saturated Model, -- |g 16.2.3. |t Building an AR(m) Filtering Matrix, -- |g 16.2.4. |t Model Selection, -- |g 16.2.5. |t Predictions and Prediction Intervals for an AR(3) Model, -- |g 16.2.6. |t Data Splitting, -- |g 16.2.7. |t Model Selection Based on a Validation Set, -- |g 16.3. |t A Modeling Example[2014]Trend and Periodicity: CO2 Levels at Mauna Lau, -- |g 16.3.1. |t The Saturated Model and Filter, -- |g 16.3.2. |t Model Selection, -- |g 16.3.3. |t How Well Does the Model Fit the Data?, -- |g 16.4. |t Modeling Periodicity with a Possible Intervention[2014]Two Examples, -- |g 16.4.1. |t The General Structure, -- |g 16.4.2. |t Directory Assistance, -- |g 16.4.3. |t Ozone Levels in Los Angeles, -- |g 14.5. |t Interpretation and Utility of the Impulse Response Function, -- |t Exercises, -- |g 15. |t The Yule[2014]Walker Equations and the Partial Autocorrelation Function -- |g 15.1. |t Background, -- |g 15.2. |t Autocovariance of an ARMA(m, l) Model, -- |g 15.2.1. |t A Preliminary Result, -- |g 15.2.2. |t The Autocovariance Function for ARMA(m, /) Models, -- |g 15.3. |t AR(m) and the Yule[2014]Walker Equations, -- |g 15.3.1. |t The Equations, -- |g 15.3.2. |t The R Function ar.yw() with an AR(3) Example, -- |g 15.3.3. |t Information Criteria-Based Model Selection Using ar.yw(), -- |g 15.4. |t The Partial Autocorrelation Plot, -- |g 15.4.1. |t A Sequence of Hypothesis Tests, -- |g 15.4.2. |t The pacf() Function[2014]Hypothesis Tests Presented in a Plot, -- |g 15.5. |t The Spectrum For Arma Processes, -- |g 15.6. |t Summary, -- |t Exercises, -- |g 16. |t Modeling Philosophy and Complete Examples -- |g 16.1. |t Modeling Overview, -- |g 16.1.1. |t The Algorithm, -- |g 16.1.2. |t The Underlying Assumption, -- |g 16.1.3. |t An Example Using an AR(m) Filter to Model MA(3), -- |g 16.1.4. |t Generalizing the "Skip" Method, -- |g 16.2. |t A Complex Periodic Model[2014]Monthly River Flows, Fumas 1931-1978, -- |g 16.2.1. |t The Data, -- |g 16.2.2. |t A Saturated Model, -- |g 16.2.3. |t Building an AR(m) Filtering Matrix, -- |g 16.2.4. |t Model Selection, -- |g 16.2.5. |t Predictions and Prediction Intervals for an AR(3) Model, -- |g 16.2.6. |t Data Splitting, -- |g 16.2.7. |t Model Selection Based on a Validation Set, -- |g 16.3. |t A Modeling Example[2014]Trend and Periodicity: CO2 Levels at Mauna Lau, -- |g 16.3.1. |t The Saturated Model and Filter, -- |g 16.3.2. |t Model Selection, -- |g 16.3.3. |t How Well Does the Model Fit the Data?, -- |g 16.4. |t Modeling Periodicity with a Possible Intervention[2014]Two Examples, -- |g 16.4.1. |t The General Structure, -- |g 16.4.2. |t Directory Assistance, -- |g 16.4.3. |t Ozone Levels in Los Angeles, -- |g 16.5. |t Periodic Models: Monthly, Weekly, and Daily Averages, -- |g 16.6. |t Summary, -- |t Exercises, -- |g 17. |t Wolf's Sunspot Number Data -- |g 17.1. |t Background, -- |g 17.2. |t Unknown Period -> Nonlinear Model, -- |g 17.3. |t The Function nls() in R, -- |g 17.4. |t Determining the Period, -- |g 17.5. |t Instability in the Mean, Amplitude, and Period, -- |g 17.6. |t Data Splitting for Prediction, -- |g 17.6.1. |t The Approach, -- |g 17.6.2. |t Step 1-Fitting One Step Ahead, -- |g 17.6.3. |t The AR Correction, -- |g 17.6.4. |t Putting it All Together, -- |g 17.6.5. |t Model Selection, -- |g 17.6.6. |t Predictions Two Steps Ahead, -- |g 17.7. |t Summary, -- |t Exercises, -- |g 18. |t An Analysis of Some Prostate and Breast Cancer Data -- |g 18.1. |t Background, -- |g 18.2. |t The First Data Set, -- |g 18.3. |t The Second Data Set, -- |g 18.3.1. |t Background and Questions, -- |g 18.3.2. |t Outline of the Statistical Analysis, -- |g 18.3.3. |t Looking at the Data, -- |g 18.3.4. |t Examining the Residuals for AR(m) Structure, -- |g 18.3.5. |t Regression Analysis with Filtered Data, -- |t Exercises, -- |g 19. |t Christopher Tennant/Ben Crosby Watershed Data -- |g 19.1. |t Background and Question, -- |g 19.2. |t Looking at the Data and Fitting Fourier Series, -- |g 19.2.1. |t The Structure of the Data, -- |g 19.2.2. |t Fourier Series Fits to the Data, -- |g 19.2.3. |t Connecting Patterns in Data to Physical Processes, -- |g 19.3. |t Averaging Data, -- |g 19.4. |t Results, -- |t Exercises, -- |g 20. |t Vostok Ice Core Data -- |g 20.1. |t Source of the Data, -- |g 20.2. |t Background, -- |g 20.3. |t Alignment, -- |g 20.3.1. |t Need for Alignment, and Possible Issues Resulting from Alignment, -- |g 20.3.2. |t Is the Pattern in the Temperature Data Maintained?, -- |g 20.3.3. |t Are the Dates Closely Matched?, -- |g 20.3.4. |t Are the Times Equally Spaced?, -- |g 20.4. |t A Naïve Analysis, -- |g 20.4.1. |t A Saturated Model, -- |g 20.4.2. |t Model Selection, -- |g 20.4.3. |t The Association Between CO2 and Temperature Change, -- |g 20.5. |t A Related Simulation, -- |g 20.5.1. |t The Model and the Question of Interest, -- |g 20.5.2. |t Simulation Code in R, -- |g 20.5.3. |t A Model Using all of the Simulated Data, -- |g 20.5.4. |t A Model Using a Sample of 283 from the Simulated Data, -- |g 20.6. |t An AR(1) Model for Irregular Spacing, -- |g 20.6.1. |t Motivation, -- |g 20.6.2. |t Method, -- |g 20.6.3. |t Results, -- |g 20.6.4. |t Sensitivity Analysis, -- |g 20.6.5. |t A Final Analysis, Well Not Quite, -- |g 20.7. |t Summary, -- |t Exercises, -- |g A.1. |t Overview, -- |g A.2. |t Loading a Time Series in Datamarket, -- |g A.3. |t Respecting Datamarket Licensing Agreements, -- |g B.1. |t Introduction, -- |g B.2. |t PRESS, -- |g B.3. |t Connection to Akaike's Result, -- |g B.4. |t Normalization and R2, -- |g B.5. |t An example, -- |g B.6. |t Conclusion and Further Comments, -- |g C.1. |t Introduction, -- |g C.2. |t Newton's Method for One-Dimensional Nonlinear Optimization, -- |g C.3. |t A Sequence of Directions, Step Sizes, and a Stopping Rule, -- |g C.4. |t What Could Go Wrong?, -- |g C.5. |t Generalizing the Optimization Problem, -- |g C.6. |t What Could Go Wrong[2014]Revisited, -- |g C.7. |t What Can be Done? |
| 542 | |f Copyright © John Wiley & Sons | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Time-series analysis |x Data processing. | |
| 650 | 0 | |a R (Computer program language) | |
| 650 | 6 | |a Série chronologique |x Informatique. | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a R (Computer program language) |2 fast | |
| 650 | 7 | |a Time-series analysis |x Data processing |2 fast | |
| 650 | 7 | |a Anàlisi de sèries temporals. |2 thub | |
| 650 | 7 | |a Processament de dades. |2 thub | |
| 650 | 7 | |a R (Llenguatge de programació) |2 thub | |
| 655 | 7 | |a Llibres electrònics. |2 thub | |
| 776 | 0 | 8 | |i Print version: |a Derryberry, DeWayne R. |t Basic data analysis for time series with R. |d Hoboken, New Jersey : John Wiley & Sons, Inc., [2014] |z 9781118422540 |w (DLC) 2014007300 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781118593363/?ar |z Texto completo |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1719581 |z Texto completo |
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