Stochastic modeling : a thorough guide to evaluate, pre-process, model and compare time series with MATLAB software /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Bonakdari, Hossein (Autor), Zeynoddin, Mohammad (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam, Netherlands : Elsevier, 2022.
Temas:
MATLAB.
MATLAB
Time-series analysis > Data processing.
Stochastic models.
S�erie chronologique > Informatique.
Mod�eles stochastiques.
Stochastic models
Time-series analysis > Data processing
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front cover
  • Half title
  • Title
  • Copyright
  • Dedication
  • Contents
  • Preface
  • Acknowledgments
  • Abbreviations
  • Chapter 1 Introduction
  • 1.1 Time series
  • 1.1.1 Time series in environmental epidemiology
  • 1.1.2 Engineering and sequential data
  • 1.1.3 Historical data for forecasting future economy
  • 1.2 Stochastic and stochastic with exogenous variables
  • 1.2.1 Stochastic models
  • 1.2.2 Stochastic model structure
  • 1.2.3 Model classifications
  • 1.3 Data preprocessing
  • 1.3.1 Definition of preprocessing
  • 1.3.2 Relationship between forecasting and time series structure
  • 1.3.3 Distribution and its impact on time series forecasting
  • References
  • Chapter 2 Preparation & stationarizing
  • 2.1 Missing data
  • 2.1.1 Linear interpolation
  • 2.1.2 Code for linear interpolation
  • 2.1.3 Spline interpolation
  • 2.1.4 Code for spline interpolation
  • 2.1.5 Modified Akima cubic Hermite interpolation
  • 2.1.6 Code for MAKIMA
  • 2.2 Detecting outliers
  • 2.2.1 Grubbs test
  • 2.2.2 Grubbs test code
  • 2.2.3 Generalized extreme studentized deviate test
  • 2.2.4 Generalized Extreme Studentized Deviate test code
  • 2.2.5 Moving average and moving median
  • 2.2.6 Moving average and moving median codes
  • 2.2.7 Quartiles and percentiles
  • 2.2.8 Quartiles and percentiles codes
  • 2.3 Time series structure and attributes
  • 2.3.1 Trend in time series
  • 2.3.2 Jump in time series
  • 2.3.3 Period in time series
  • 2.4 Stationarity
  • 2.4.1 Unit root tests for stationarity evaluation
  • 2.4.2 Augmented Dickey-Fuller test
  • 2.4.3 KPSS test
  • 2.4.4 Phillips-Perron test
  • 2.4.5 Complementary adjustments for stationary test functions
  • 2.5 Deterministic terms detection tests
  • 2.5.1 Mann-Kendal test
  • 2.5.2 Mann-Whitney test
  • 2.5.3 Fisher's g test
  • 2.5.4 Correlograms
  • 2.5.5 How to determine the nonseasonal or seasonal correlations and the periodicity in time series by using correlograms?
  • 2.6 Stationarizing methods
  • 2.6.1 Trend analysis
  • 2.6.2 Differencing
  • 2.6.3 Standardization
  • 2.6.4 Spectral analysis
  • 2.7 Exercise
  • References
  • Chapter 3 Distribution evaluation and normalizing
  • 3.1 Distribution visualization
  • 3.2 Normal distribution definition
  • 3.3 Skewness
  • 3.4 Kurtosis
  • 3.5 Common tests and transforms
  • 3.6 Data distribution tests
  • 3.6.1 Graphical methods
  • 3.6.2 Skewness and kurtosis
  • 3.6.3 Anderson-Darling test
  • 3.6.4 Lillifors test
  • 3.6.5 Jarque-Bera test
  • 3.6.6 Shapiro-Wilk test
  • 3.7 Normalization transforms
  • 3.7.1 Logarithmic
  • 3.7.2 Standard logarithmic
  • 3.7.3 Box-Cox
  • 3.7.4 Yeo-Johnson
  • 3.7.5 John-Draper
  • 3.7.6 Manly
  • 3.8 Exercise
  • References
  • Chapter 4 Stochastic modeling
  • 4.1 Modeling methods overview
  • 4.2 Deterministic models
  • 4.3 Probabilistic statistical models
  • 4.4 Stochastic concepts
  • 4.5 Differencing operators in stochastic models
  • 4.5.1 Nonseasonal differencing
  • 4.5.2 Seasonal differencing
  • 4.6 Stochastic models equations

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