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Biblioteca

MARC Lista (tabellare)
Analysis and Data-Based Reconstruction of Complex Nonlinear Dynamical Systems : Using the Methods of Stochastic Processes / / by M. Reza Rahimi Tabar
Analysis and Data-Based Reconstruction of Complex Nonlinear Dynamical Systems : Using the Methods of Stochastic Processes / / by M. Reza Rahimi Tabar
Autore Rahimi Tabar M. Reza
Edizione [1st ed. 2019.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Descrizione fisica 1 online resource (XVIII, 280 p. 41 illus., 22 illus. in color.)
Disciplina 519.2
519.23
Collana Understanding Complex Systems
Soggetto topico Processos estocàstics
Sistemes complexos
Anàlisi de sèries temporals
Statistical physics
Dynamics
System theory
Probabilities
Economics
Computational complexity
Neurosciences
Complex Systems
Probability Theory and Stochastic Processes
Economic Theory/Quantitative Economics/Mathematical Methods
Complexity
Soggetto genere / forma Llibres electrònics
ISBN 3-030-18472-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1 Introduction -- 2 Introduction to Stochastic Processes -- 3 Kramers-Moyal Expansion and Fokker-Planck Equation -- 4 Continuous Stochastic Process -- 5 The Langevin Equation and Wiener Process -- 6 Stochastic Integration, It^o and Stratonovich Calculi -- 7 Equivalence of Langevin and Fokker-Planck Equations -- 8 Examples of Stochastic Calculus -- 9 Langevin Dynamics in Higher Dimensions -- 10 Levy Noise Driven Langevin Equation and its Time Series-Based Reconstruction -- 11 Stochastic Processes with Jumps and Non-Vanishing Higher-Order Kramers-Moyal Coefficients -- 12 Jump-Diffusion Processes -- 13 Two-Dimensional (Bivariate) Jump-Diffusion Processes -- 14 Numerical Solution of Stochastic Differential Equations: Diffusion and Jump-Diffusion Processes -- 15 The Friedrich-Peinke Approach to Reconstruction of Dynamical Equation for Time Series: Complexity in View of Stochastic Processes -- 16 How To Set Up Stochastic Equations For Real-World Processes: Markov-Einstein Time Scale -- 17 Reconstruction of Stochastic Dynamical Equations: Exemplary Stationary Diffusion and Jump-Diffusion Processes -- 18 The Kramers-Moyal Coefficients of Non-Stationary Time series in The Presence of Microstructure (Measurement) Noise -- 19 Influence of Finite Time Step in Estimating of the Kramers-Moyal Coefficients -- 20 Distinguishing Diffusive and Jumpy Behaviors in Real-World Time Series -- 21 Reconstruction of Langevin and Jump-Diffusion Dynamics From Empirical Uni- and Bivariate Time Series -- 22 Applications and Outlook -- 23 Epileptic Brain Dynamics.
Record Nr. UNINA-9910337876203321
Rahimi Tabar M. Reza  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied Time Series Analysis and Forecasting with Python [[electronic resource] /] / by Changquan Huang, Alla Petukhina
Applied Time Series Analysis and Forecasting with Python [[electronic resource] /] / by Changquan Huang, Alla Petukhina
Autore Huang Changquan
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Descrizione fisica 1 online resource (377 pages)
Disciplina 813
Collana Statistics and Computing
Soggetto topico Time-series analysis
Statistics - Computer programs
Econometrics
Python (Computer program language)
Machine learning
Statistics
Time Series Analysis
Statistical Software
Python
Machine Learning
Statistics in Business, Management, Economics, Finance, Insurance
Anàlisi de sèries temporals
Python (Llenguatge de programació)
Soggetto genere / forma Llibres electrònics
ISBN 3-031-13584-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Time Series Concepts and Python -- 2. Exploratory Time Series Data Analysis -- 3. Stationary Time Series Models -- 4. ARMA and ARIMA Modeling and Forecasting -- 5. Nonstationary Time Series Models -- 6. Financial Time Series and Related Models -- 7. Multivariate Time Series Analysis -- 8. State Space Models and Markov Switching Models -- 9. Nonstationarity and Cointegrations -- 10. Modern Machine Learning Methods for Time Series Analysis.
Record Nr. UNISA-996495169403316
Huang Changquan  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Applied Time Series Analysis and Forecasting with Python / / by Changquan Huang, Alla Petukhina
Applied Time Series Analysis and Forecasting with Python / / by Changquan Huang, Alla Petukhina
Autore Huang Changquan
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Descrizione fisica 1 online resource (377 pages)
Disciplina 813
519.55
Collana Statistics and Computing
Soggetto topico Time-series analysis
Statistics - Computer programs
Econometrics
Python (Computer program language)
Machine learning
Statistics
Time Series Analysis
Statistical Software
Python
Machine Learning
Statistics in Business, Management, Economics, Finance, Insurance
Anàlisi de sèries temporals
Python (Llenguatge de programació)
Soggetto genere / forma Llibres electrònics
ISBN 3-031-13584-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Time Series Concepts and Python -- 2. Exploratory Time Series Data Analysis -- 3. Stationary Time Series Models -- 4. ARMA and ARIMA Modeling and Forecasting -- 5. Nonstationary Time Series Models -- 6. Financial Time Series and Related Models -- 7. Multivariate Time Series Analysis -- 8. State Space Models and Markov Switching Models -- 9. Nonstationarity and Cointegrations -- 10. Modern Machine Learning Methods for Time Series Analysis.
Record Nr. UNINA-9910619280803321
Huang Changquan  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
Continuous Time Processes for Finance [[electronic resource] ] : Switching, Self-exciting, Fractional and other Recent Dynamics / / by Donatien Hainaut
Continuous Time Processes for Finance [[electronic resource] ] : Switching, Self-exciting, Fractional and other Recent Dynamics / / by Donatien Hainaut
Autore Hainaut Donatien
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Descrizione fisica 1 online resource (359 pages)
Disciplina 332.015195
Collana Bocconi & Springer Series, Mathematics, Statistics, Finance and Economics
Soggetto topico Probabilities
Social sciences - Mathematics
Econometrics
Actuarial science
Probability Theory
Mathematics in Business, Economics and Finance
Actuarial Mathematics
Quantitative Economics
Finances
Models matemàtics
Estadística matemàtica
Processos estocàstics
Anàlisi de sèries temporals
Soggetto genere / forma Llibres electrònics
ISBN 9783031063619
9783031063602
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface -- Acknowledgements -- Notations -- 1. Switching Models: Properties and Estimation -- 2. Estimation of Continuous Time Processes by Markov Chain Monte Carlo -- 3. Particle Filtering and Estimation -- 4. Modeling of Spillover Effects in Stock Markets -- 5. Non-Markov Models for Contagion and Spillover -- 6. Fractional Brownian Motion -- 7. Gaussian Fields for Asset Prices -- 8. Lévy Interest Rate Models With a Long Memory -- 9. Affine Volterra Processes and Rough Models -- 10. Sub-Diffusion for Illiquid Markets -- 11. A Fractional Dupire Equation for Jump-Diffusions -- References.
Record Nr. UNISA-996485661303316
Hainaut Donatien  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Continuous Time Processes for Finance : Switching, Self-exciting, Fractional and other Recent Dynamics / / by Donatien Hainaut
Continuous Time Processes for Finance : Switching, Self-exciting, Fractional and other Recent Dynamics / / by Donatien Hainaut
Autore Hainaut Donatien
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Descrizione fisica 1 online resource (359 pages)
Disciplina 332.015195
Collana Bocconi & Springer Series, Mathematics, Statistics, Finance and Economics
Soggetto topico Probabilities
Social sciences - Mathematics
Econometrics
Actuarial science
Probability Theory
Mathematics in Business, Economics and Finance
Actuarial Mathematics
Quantitative Economics
Finances
Models matemàtics
Estadística matemàtica
Processos estocàstics
Anàlisi de sèries temporals
Soggetto genere / forma Llibres electrònics
ISBN 9783031063619
9783031063602
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface -- Acknowledgements -- Notations -- 1. Switching Models: Properties and Estimation -- 2. Estimation of Continuous Time Processes by Markov Chain Monte Carlo -- 3. Particle Filtering and Estimation -- 4. Modeling of Spillover Effects in Stock Markets -- 5. Non-Markov Models for Contagion and Spillover -- 6. Fractional Brownian Motion -- 7. Gaussian Fields for Asset Prices -- 8. Lévy Interest Rate Models With a Long Memory -- 9. Affine Volterra Processes and Rough Models -- 10. Sub-Diffusion for Illiquid Markets -- 11. A Fractional Dupire Equation for Jump-Diffusions -- References.
Record Nr. UNINA-9910590077503321
Hainaut Donatien  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data Driven Model Learning for Engineers : With Applications to Univariate Time Series / / by Guillaume Mercère
Data Driven Model Learning for Engineers : With Applications to Univariate Time Series / / by Guillaume Mercère
Autore Mercère Guillaume
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (X, 212 p. 93 illus., 54 illus. in color.)
Disciplina 519.55
620.00151955
Soggetto topico Time-series analysis
Machine learning
Statistics
Time Series Analysis
Statistical Learning
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Enginyeria
Models matematics
Processament de dades
Anàlisi de sèries temporals
Soggetto genere / forma Llibres electrònics
ISBN 3-031-31636-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910741194703321
Mercère Guillaume  
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Diagnostic methods in time series / / Fumiya Akashi [and three others]
Diagnostic methods in time series / / Fumiya Akashi [and three others]
Autore Akashi Fumiya
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Gateway East, Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (X, 108 p. 17 illus., 10 illus. in color.)
Disciplina 519.55
Collana JSS Research Series in Statistics
Soggetto topico Time-series analysis
Anàlisi de sèries temporals
Soggetto genere / forma Llibres electrònics
ISBN 981-16-2264-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1. Elements of Stochastic Processes -- Chapter 2. Systematic approach for portmanteau tests in view of Whittle likelihood ratio -- Chapter 3. A new look at portmanteau test -- Chapter 4. Adjustments for a class of tests under nonstandard conditions -- Chapter 5. Adjustments for variance component tests in ANOVA models -- Chapter 6. Robust causality test of infinite variance processes.
Record Nr. UNINA-9910483157503321
Akashi Fumiya  
Gateway East, Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Diagnostic methods in time series / / Fumiya Akashi [and three others]
Diagnostic methods in time series / / Fumiya Akashi [and three others]
Autore Akashi Fumiya
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Gateway East, Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (X, 108 p. 17 illus., 10 illus. in color.)
Disciplina 519.55
Collana JSS Research Series in Statistics
Soggetto topico Time-series analysis
Anàlisi de sèries temporals
Soggetto genere / forma Llibres electrònics
ISBN 981-16-2264-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1. Elements of Stochastic Processes -- Chapter 2. Systematic approach for portmanteau tests in view of Whittle likelihood ratio -- Chapter 3. A new look at portmanteau test -- Chapter 4. Adjustments for a class of tests under nonstandard conditions -- Chapter 5. Adjustments for variance component tests in ANOVA models -- Chapter 6. Robust causality test of infinite variance processes.
Record Nr. UNISA-996466414003316
Akashi Fumiya  
Gateway East, Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui