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Basic data analysis for time series with R / / DeWayne R. Derryberry
| Basic data analysis for time series with R / / DeWayne R. Derryberry |
| Autore | Derryberry DeWayne R. |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (320 p.) |
| Disciplina | 001.4/2202855133 |
| Soggetto topico |
Time-series analysis - Data processing
R (Computer program language) Anàlisi de sèries temporals Processament de dades R (Llenguatge de programació) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
1-118-59337-5
1-118-59323-5 1-118-59336-7 |
| Classificazione | MAT029000 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine generated contents note: Part I - Basic correlation structures Chapter 0 - R basics 0.1 Getting started 0.2 Special R conventions 0.3 Common structures 0.4 Common functions 0.5 Time series functions 0.6 Importing data Chapter 1 - Review of regression and more about R 1.1 Goals of this chapter 1.2 The simple(st) regression model 1.3 Simulating the data from a model and estimating the model parameters in R 1.4 Basic inference for the model 1.5 Residuals analysis - What can go wrong... 1.6 Matrix manipulation in R Chapter 2 - The modeling approach taken in this book and some examples of typical serially correlated data 2.1 Signal and noise 2.2 Time series data 2.3 Simple regression in the framework 2.4 Real data and simulated data 2.5 The diversity of time series data 2.6 Getting data into R Chapter 3 - Some comments on assumptions 3.1 Introduction 3.2 The normality assumption 3.3 Equal variance 3.4 Independence 3.5 Power of logarithmic transformations illustrated 3.6 Summary Chapter 4 - The autocorrelation function and AR(1), AR(2) models 4.1 Standard models - What are the alternatives to white noise? 4.2 Autocovariance and autocorrelation 4.3 The acf() function in R 4.4 The first alternative to white noise: Autoregressive errors - AR(1), AR(2) Chapter 5 - The moving average models MA(1) and MA(2) 5.1 The moving average model 5.2 The autocorrelation for MA(1) models 5.3 A duality between MA(l) and AR(m) models 5.4 The autocorrelation for MA(2) models 5.5 Simulated examples of the MA(1) model 5.5 Simulated examples of the MA(2) model 5.6 AR(m) and MA(l) model acf() plots Part II - Analysis of periodic data and model selection Chapter 6 - Review of transcendental functions and complex numbers 6.1 Background 6.2 Complex arithmetic 6.3 Some important series 6.4 Useful facts about periodic transcendental functions Chapter 7 - The power spectrum and the periodogram 7.1 Introduction 7.2 A definition and a simplified form for p(f) 7.3 Inverting p(f) to recover the Ck values 7.4 The power spectrum for some familiar models 7.5 The periodogram, a closer look 7.6 The function spec.pgram() in R Chapter 8 - Smoothers, the bias-variance tradeoff, and the smoothed periodogram 8.1 Why is smoothing required? 8.2 Smoothing, bias, and variance 8.3 Smoothers used in R 8.4 Smoothing the periodogram for a series with a known period or unknown period. 8.5 Summary Chapter 9 - A regression model for periodic data. 9.1 The model 9.2 An example: the NYC temperature data 9.2 Complications 1: CO2 data 9.3 Complications 2: Sunspots 9.4 Complications 3: Accidental Deaths 9.5 Summary Chapter 10 - Basic model selection and cross validation. 10.1 Background 10.2 Hypothesis tests in simple regression 10.3 A more general setting for likelihood ratio tests 10.4 A subtlety different situation 10.5 Information criteria 10.6 Cross validation (Data splitting): NYC temperatures 10.7 Summary Chapter 11 - Fitting some Fourier series 11.1 Introduction: more complex periodic models 11.2 More complex periodic behavior: Accidental deaths 11.3 The Boise river flow data 11.4 Where do we go from here? Chapter 12 - Adjusting for AR(1) correlation in complex models 12.1 Introduction 12.2 The two sample t-test - Uncut and patch cut forest 12.3 The second Sleuth case - Global warming, a simple regression 12.4 The Semmelweis intervention 12.5 The NYC temperatures (adjusted) 12.6 The Boise river flow data: model selection with filtering 12.7 Implications of AR(1) adjustments and the "skip" method 12.8 Summary Part III - Complex temporal structures Chapter 13 - The backshift operator, the impulse response function, and general ARMA models 13.1 The general ARMA model 13.2 The backshift (shift, lag) operator 13.3 The impulse response operator - intuition 13.4 Impulse response operator, g(B) - computation 13.5 Interpretation and utility of the impulse response function Chapter 14 - The Yule-Walker equations and the partial autocorrelation function. 14.1 Background 14.2 Autocovariance of an ARMA(m,l) model 14.3 AR(m) and the Yule-Walker equations 14.4 The partial autocorrelation plot 14.5 The spectrum for ARMA processes 14.6 Summary Chapter 15 - Modeling philosophy and complete examples 15.1 Modeling overview 15.2 A complex periodic model - Monthly river flows, Furnas 1931-1978 15.3 A modeling example - trend and periodicity: CO2 levels at Mauna Lau 15.4 Modeling periodicity with a possible intervention - two examples 15.5 Periodic models: monthly, weekly, and daily averages 15.6 Summary Part IV - Some detailed and complete examples Chapter 16 - the Wolf sunspot number data 16.1 Background 16.2 Unknown period => nonlinear model 16.3 The function nls() in R 16.4 Determining the period 16.5 Instability in the mean, amplitude, and period 16.6 Data splitting for prediction 16.7 Summary Chapter 17 - Analysis of prostate and breast cancer data 17.1 Background 17.2 The first data set 17.3 The second data set Chapter 18 - Christopher Tennant/Ben Crosby watershed data 18.1 Background and question 18.2 Looking at the data and fitting Fourier series 18.3 Averaging data 18.4 Results Chapter 19 - Vostok ice core data 19.1 Source of the data 19.2 Background 19.3 Alignment 19.4 A naïve analysis 19.5 A related simulation 19.6 An AR(1) model for irregular spacing 19.7 Summary Appendices Appendix 1 - Using Data Market A1.1 Overview A1.2 Loading a time series in DataMarket A1.3 Respecting DataMarket licensing agreements Appendix 2 - AIC is PRESS A2.1 Introduction A2.2 PRESS A2.3 Connection to Akaike's result A2.4 Normalization and R2 A2.5 An example A2.6 Conclusion and further comments Appendix 3 - A 15 minute tutorial on optimization and nonlinear regression A3.1 Introduction A3.2 Newton's method for one dimensional nonlinear optimization A3.3 A direction, a step size, and a stopping rule A3.4 What could go wrong? A3.5 Generalizing the optimization problem A3.6 What could go wrong revisited A3.7 What can be done? . |
| Record Nr. | UNINA-9910132187103321 |
Derryberry DeWayne R.
|
||
| Hoboken, New Jersey : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Basic data analysis for time series with R / / DeWayne R. Derryberry
| Basic data analysis for time series with R / / DeWayne R. Derryberry |
| Autore | Derryberry DeWayne R. |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (320 p.) |
| Disciplina | 001.4/2202855133 |
| Soggetto topico |
Time-series analysis - Data processing
R (Computer program language) Anàlisi de sèries temporals Processament de dades R (Llenguatge de programació) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
1-118-59337-5
1-118-59323-5 1-118-59336-7 |
| Classificazione | MAT029000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine generated contents note: Part I - Basic correlation structures Chapter 0 - R basics 0.1 Getting started 0.2 Special R conventions 0.3 Common structures 0.4 Common functions 0.5 Time series functions 0.6 Importing data Chapter 1 - Review of regression and more about R 1.1 Goals of this chapter 1.2 The simple(st) regression model 1.3 Simulating the data from a model and estimating the model parameters in R 1.4 Basic inference for the model 1.5 Residuals analysis - What can go wrong... 1.6 Matrix manipulation in R Chapter 2 - The modeling approach taken in this book and some examples of typical serially correlated data 2.1 Signal and noise 2.2 Time series data 2.3 Simple regression in the framework 2.4 Real data and simulated data 2.5 The diversity of time series data 2.6 Getting data into R Chapter 3 - Some comments on assumptions 3.1 Introduction 3.2 The normality assumption 3.3 Equal variance 3.4 Independence 3.5 Power of logarithmic transformations illustrated 3.6 Summary Chapter 4 - The autocorrelation function and AR(1), AR(2) models 4.1 Standard models - What are the alternatives to white noise? 4.2 Autocovariance and autocorrelation 4.3 The acf() function in R 4.4 The first alternative to white noise: Autoregressive errors - AR(1), AR(2) Chapter 5 - The moving average models MA(1) and MA(2) 5.1 The moving average model 5.2 The autocorrelation for MA(1) models 5.3 A duality between MA(l) and AR(m) models 5.4 The autocorrelation for MA(2) models 5.5 Simulated examples of the MA(1) model 5.5 Simulated examples of the MA(2) model 5.6 AR(m) and MA(l) model acf() plots Part II - Analysis of periodic data and model selection Chapter 6 - Review of transcendental functions and complex numbers 6.1 Background 6.2 Complex arithmetic 6.3 Some important series 6.4 Useful facts about periodic transcendental functions Chapter 7 - The power spectrum and the periodogram 7.1 Introduction 7.2 A definition and a simplified form for p(f) 7.3 Inverting p(f) to recover the Ck values 7.4 The power spectrum for some familiar models 7.5 The periodogram, a closer look 7.6 The function spec.pgram() in R Chapter 8 - Smoothers, the bias-variance tradeoff, and the smoothed periodogram 8.1 Why is smoothing required? 8.2 Smoothing, bias, and variance 8.3 Smoothers used in R 8.4 Smoothing the periodogram for a series with a known period or unknown period. 8.5 Summary Chapter 9 - A regression model for periodic data. 9.1 The model 9.2 An example: the NYC temperature data 9.2 Complications 1: CO2 data 9.3 Complications 2: Sunspots 9.4 Complications 3: Accidental Deaths 9.5 Summary Chapter 10 - Basic model selection and cross validation. 10.1 Background 10.2 Hypothesis tests in simple regression 10.3 A more general setting for likelihood ratio tests 10.4 A subtlety different situation 10.5 Information criteria 10.6 Cross validation (Data splitting): NYC temperatures 10.7 Summary Chapter 11 - Fitting some Fourier series 11.1 Introduction: more complex periodic models 11.2 More complex periodic behavior: Accidental deaths 11.3 The Boise river flow data 11.4 Where do we go from here? Chapter 12 - Adjusting for AR(1) correlation in complex models 12.1 Introduction 12.2 The two sample t-test - Uncut and patch cut forest 12.3 The second Sleuth case - Global warming, a simple regression 12.4 The Semmelweis intervention 12.5 The NYC temperatures (adjusted) 12.6 The Boise river flow data: model selection with filtering 12.7 Implications of AR(1) adjustments and the "skip" method 12.8 Summary Part III - Complex temporal structures Chapter 13 - The backshift operator, the impulse response function, and general ARMA models 13.1 The general ARMA model 13.2 The backshift (shift, lag) operator 13.3 The impulse response operator - intuition 13.4 Impulse response operator, g(B) - computation 13.5 Interpretation and utility of the impulse response function Chapter 14 - The Yule-Walker equations and the partial autocorrelation function. 14.1 Background 14.2 Autocovariance of an ARMA(m,l) model 14.3 AR(m) and the Yule-Walker equations 14.4 The partial autocorrelation plot 14.5 The spectrum for ARMA processes 14.6 Summary Chapter 15 - Modeling philosophy and complete examples 15.1 Modeling overview 15.2 A complex periodic model - Monthly river flows, Furnas 1931-1978 15.3 A modeling example - trend and periodicity: CO2 levels at Mauna Lau 15.4 Modeling periodicity with a possible intervention - two examples 15.5 Periodic models: monthly, weekly, and daily averages 15.6 Summary Part IV - Some detailed and complete examples Chapter 16 - the Wolf sunspot number data 16.1 Background 16.2 Unknown period => nonlinear model 16.3 The function nls() in R 16.4 Determining the period 16.5 Instability in the mean, amplitude, and period 16.6 Data splitting for prediction 16.7 Summary Chapter 17 - Analysis of prostate and breast cancer data 17.1 Background 17.2 The first data set 17.3 The second data set Chapter 18 - Christopher Tennant/Ben Crosby watershed data 18.1 Background and question 18.2 Looking at the data and fitting Fourier series 18.3 Averaging data 18.4 Results Chapter 19 - Vostok ice core data 19.1 Source of the data 19.2 Background 19.3 Alignment 19.4 A naïve analysis 19.5 A related simulation 19.6 An AR(1) model for irregular spacing 19.7 Summary Appendices Appendix 1 - Using Data Market A1.1 Overview A1.2 Loading a time series in DataMarket A1.3 Respecting DataMarket licensing agreements Appendix 2 - AIC is PRESS A2.1 Introduction A2.2 PRESS A2.3 Connection to Akaike's result A2.4 Normalization and R2 A2.5 An example A2.6 Conclusion and further comments Appendix 3 - A 15 minute tutorial on optimization and nonlinear regression A3.1 Introduction A3.2 Newton's method for one dimensional nonlinear optimization A3.3 A direction, a step size, and a stopping rule A3.4 What could go wrong? A3.5 Generalizing the optimization problem A3.6 What could go wrong revisited A3.7 What can be done? . |
| Record Nr. | UNINA-9910822358103321 |
|
Derryberry DeWayne R.
|
||
| Hoboken, New Jersey : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Change detection and image time-series analysis . 1 : unsupervised methods / / Abdourrahmane M. Atto, Francesca Bovolo, Lorenzo Bruzzone
| Change detection and image time-series analysis . 1 : unsupervised methods / / Abdourrahmane M. Atto, Francesca Bovolo, Lorenzo Bruzzone |
| Autore | Atto Abdourrahmane M. |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc., , [2022] |
| Descrizione fisica | 1 online resource (304 pages) |
| Disciplina | 519.55 |
| Soggetto topico |
Time-series analysis
Time-series analysis - Data processing |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-88225-7
1-119-88226-5 1-119-88224-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface -- List of Notations -- Chapter 1. Unsupervised Change Detection in Multitemporal Remote Sensing Images -- 1.1. Introduction -- 1.2. Unsupervised change detection in multispectral images -- 1.2.1. Related concepts -- 1.2.2. Open issues and challenges -- 1.2.3. Spectral-spatial unsupervised CD techniques -- 1.3. Unsupervised multiclass change detection approaches based on modeling spectral-spatial information -- 1.3.1. Sequential spectral change vector analysis (S2CVA) -- 1.3.2. Multiscale morphological compressed change vector analysis -- 1.3.3. Superpixel-level compressed change vector analysis -- 1.4. Dataset description and experimental setup -- 1.4.1. Dataset description -- 1.4.2. Experimental setup -- 1.5. Results and discussion -- 1.5.1. Results on the Xuzhou dataset -- 1.5.2. Results on the Indonesia tsunami dataset -- 1.6. Conclusion -- 1.7. Acknowledgements -- 1.8. References -- Chapter 2. Change Detection inTime Series of Polarimetric SAR Images -- 2.1. Introduction -- 2.1.1. The problem -- 2.1.2. Important concepts illustrated bymeans of the gamma distribution -- 2.2. Test theory and matrix ordering -- 2.2.1. Test for equality of two complex Wishart distributions -- 2.2.2. Test for equality of k-complex Wishart distributions -- 2.2.3. The block diagonal case -- 2.2.4. The Loewner order -- 2.3. The basic change detection algorithm -- 2.4. Applications -- 2.4.1. Visualizing changes -- 2.4.2. Fieldwise change detection -- 2.4.3. Directional changes using the Loewner ordering -- 2.4.4. Software availability -- 2.5. References -- Chapter 3. An Overview of Covariance-based Change Detection Methodologies in Multivariate SAR Image Time Series -- 3.1. Introduction -- 3.2. Dataset description -- 3.3. Statistical modeling of SAR images -- 3.3.1. The data.
3.3.2. Gaussian model -- 3.3.3. Non-Gaussian modeling -- 3.4. Dissimilarity measures -- 3.4.1. Problem formulation -- 3.4.2. Hypothesis testing statistics -- 3.4.3. Information-theoretic measures -- 3.4.4. Riemannian geometry distances -- 3.4.5. Optimal transport -- 3.4.6. Summary -- 3.4.7. Results of change detectors on the UAVSAR dataset -- 3.5. Change detection based on structured covariances -- 3.5.1. Low-rank Gaussian change detector -- 3.5.2. Low-rank compound Gaussian change detector -- 3.5.3. Results of low-rank change detectors on the UAVSAR dataset -- 3.6. Conclusion -- 3.7. References -- Chapter 4. Unsupervised Functional Information Clustering in Extreme Environments from Filter Banks and Relative Entropy -- 4.1. Introduction -- 4.2. Parametric modeling of convnet features -- 4.3. Anomaly detection in image time series -- 4.4. Functional image time series clustering -- 4.5. Conclusion -- 4.6. References -- Chapter 5. Thresholds and Distances to Better Detect Wet Snow over Mountains with Sentinel-1 Image Time Series -- 5.1. Introduction -- 5.2. Test area and data -- 5.3. Wet snow detection using Sentinel-1 -- 5.4. Metrics to detect wet snow -- 5.5. Discussion -- 5.6. Conclusion -- 5.7. Acknowledgements -- 5.8. References -- Chapter 6. Fractional Field Image Time Series Modeling and Application to Cyclone Tracking -- 6.1. Introduction -- 6.2. Random field model of a cyclone texture -- 6.2.1. Cyclone texture feature -- 6.2.2. Wavelet-based power spectral densities and cyclone -- 6.2.3. Fractional spectral power decay model -- 6.3. Cyclone field eye detection and tracking -- 6.3.1. Cyclone eye detection -- 6.3.2. Dynamic fractal field eye tracking -- 6.4. Cyclone field intensity evolution prediction -- 6.5. Discussion -- 6.6. Acknowledgements -- 6.7. References. Chapter 7. Graph of Characteristic Points for Texture Tracking: Application to Change Detection and Glacier Flow Measurement from SAR Image -- 7.1. Introduction -- 7.2. Texture representation and characterization using local extrema -- 7.2.1. Motivation and approach -- 7.2.2. Local extrema keypoints within SAR images -- 7.3. Unsupervised change detection -- 7.3.1. Proposed framework -- 7.3.2. Weighted graph construction from keypoints -- 7.3.3. Change measure (CM) generation -- 7.4. Experimental study -- 7.4.1. Data description and evaluation criteria -- 7.4.2. Change detection results -- 7.4.3. Sensitivity to parameters -- 7.4.4. Comparison with the NLM model -- 7.4.5. Analysis of the algorithm complexity -- 7.5. Application to glacier flow measurement -- 7.5.1. Proposed method -- 7.5.2. Results -- 7.6. Conclusion -- 7.7. References -- Chapter 8. Multitemporal Analysis of Sentinel-1/2 Images for Land Use Monitoring at Regional Scale -- 8.1. Introduction -- 8.2. Proposed method -- 8.2.1. Test site and data -- 8.3. SAR processing -- 8.4. Optical processing -- 8.5. Combination layer -- 8.6. Results -- 8.7. Conclusion -- 8.8. References -- Chapter 9. Statistical Difference Models for Change Detection in Multispectral Images -- 9.1. Introduction -- 9.2. Overview of the change detection problem -- 9.2.1. Change detection methods for multispectral images -- 9.2.2. Challenges addressed in this chapter -- 9.3. The Rayleigh-Rice mixture model for the magnitude of the difference image -- 9.3.1. Magnitude image statistical mixture model -- 9.3.2. Bayesian decision -- 9.3.3. Numerical approach to parameter estimation -- 9.4. A compound multiclass statistical model of the difference image -- 9.4.1. Difference image statistical mixture model -- 9.4.2. Magnitude image statistical mixture model -- 9.4.3. Bayesian decision. 9.4.4. Numerical approach to parameter estimation -- 9.5. Experimental results -- 9.5.1. Dataset description -- 9.5.2. Experimental setup -- 9.5.3. Test 1: Two-class Rayleigh-Rice mixture model -- 9.5.4. Test 2: Multiclass Rician mixture model -- 9.6. Conclusion -- 9.7. References -- List of Authors -- Index -- Summary of Volume 2 -- EULA. |
| Record Nr. | UNINA-9910554803303321 |
|
Atto Abdourrahmane M.
|
||
| Hoboken, NJ : , : John Wiley & Sons, Inc., , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Displaying time series, spatial, and space-time data with R / Oscar Perpinan Lamigueiro
| Displaying time series, spatial, and space-time data with R / Oscar Perpinan Lamigueiro |
| Autore | Perpinan Lamigueiro, Oscar |
| Pubbl/distr/stampa | Boca Raton : CRC, 2014 |
| Descrizione fisica | VII, 200 p. ; 24 cm |
| Disciplina | 519.5502855133 |
| Collana | Chapman & Hall/CRC the R series |
| Soggetto topico |
Time-series analysis - Data processing
R (Computer program language) |
| ISBN | 9781466565203 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISALENTO-991002566699707536 |
|
Perpinan Lamigueiro, Oscar
|
||
| Boca Raton : CRC, 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
| ||
Time Series Algorithms Recipes : Implement Machine Learning and Deep Learning Techniques with Python / / by Akshay R Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V Adithya Krishnan
| Time Series Algorithms Recipes : Implement Machine Learning and Deep Learning Techniques with Python / / by Akshay R Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V Adithya Krishnan |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023 |
| Descrizione fisica | 1 online resource (188 pages) |
| Disciplina | 006.31 |
| Soggetto topico |
Time-series analysis - Computer programs
Time-series analysis - Data processing Machine learning - Computer programs Python (Computer program language) |
| ISBN |
9781484289785
1484289781 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Getting Started with Time Series -- Chapter 2: Statistical Univariate Modelling -- Chapter 3: Statistical Multivariate Modelling -- Chapter 4: Machine Learning Regression-Based Forecasting -- Chapter 5: Forecasting Using Deep Learning. |
| Record Nr. | UNINA-9910739454703321 |
| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
