Details

Autor(en) / Beteiligte

• Bonakdari, Hossein,
• Zeynoddin, Mohammad,

Titel

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

Ort / Verlag

Amsterdam, Netherlands : Elsevier Inc.,

Erscheinungsjahr

[2022]

Beschreibungen/Notizen

• Includes index.
• 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 &amp -- 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 -- 4.6.1 General relationships -- 4.6.2 Polynomial equations -- 4.7 Identify appropriate models and parameters' orders -- 4.8 Estimation of stochastic models' parameters -- 4.9 Univariate stochastic modeling -- 4.9.1 Model creation -- 4.9.2 Polynomial parameter estimation -- 4.9.3 Extracting residuals and modeled series -- 4.9.4 Presampling in stochastic models -- 4.9.5 Optimization methods in stochastic models -- 4.9.6 Forecasting future steps -- 4.10 Stochastic models with exogenous inputs -- 4.10.1 General relationships -- 4.10.2 StochasticX models with initial parameters -- 4.11 Fitting stochastic and stochasticX models by econometric modeler app.
• 4.11.1 Loading data in MATLAB environment and opening econometric modeler -- 4.11.2 Using econometric modeler -- 4.12 Invertibility constraint for MA models -- 4.13 Chapter summary -- 4.14 Exercise -- References -- Chapter 5 Goodness-of-fit &amp -- precision criteria -- 5.1 Model adequacy -- 5.1.1 Visual tools: residuals ACF and cumulative periodogram -- 5.1.2 Numerical tests -- 5.2 Model parsimony -- 5.2.1 Akaike's information criterion -- 5.2.2 Bayesian information criterion -- 5.2.3 Parameters' significance test -- 5.3 Conventional performance measure -- 5.4 Cross-validation in time series -- 5.4.1 Hold-out method -- 5.4.2 Leave-p-out cross-validation (LPO-CV) -- 5.4.3 Leave-one-out cross-validation (LOO-CV) -- 5.4.4 k-fold cross-validation -- 5.4.5 Stratified k-fold cross-validation -- 5.4.6 Time series cross-validation -- 5.5 Exercise -- References -- Chapter 6 Forecasting time series by deep learning and hybrid methods -- 6.1 Deep learning introduction -- 6.1.1 Long-short term memory modeling concepts -- 6.1.2 Forecasting time series using long short-term memory model -- 6.1.3 Forecast the time series using dynamic long short-term memory model -- 6.2 Hybrid modeling -- 6.2.1 Hybridization concepts -- 6.3 Exercise -- References -- Appendix MATLAB introduction and basic commands -- A.1 Introduction -- A.2 How to execute commands in MATLAB -- A.3 Write commands in the Command Window -- A.4 Frequently used commands -- A.5 Using MATLAB's help -- A.6 Arithmetic operators -- A.7 Commonly used characters, variables, and constants -- A.8 Relational operators -- A.9 Logical operators -- A.10 m Files -- A.11 Functions -- A.12 MATLAB's predefined functions -- A.12.1 Opening m file of predefined functions -- A.13 Anonymous functions -- A.14 Symbolic function -- A.14.1 double function -- A.14.2 Piecewise function -- A.15 The most common built-in functions.
• A.15.1 Import/export functions -- A.15.2 figure function -- A.15.3 plot function -- A.15.4 legend function -- A.15.5 title, xlabel, ylabel functions -- A.15.6 Hold on/off functions -- A.15.7 xlim and ylim functions -- A.15.8 Subplot function -- A.15.9 disp function -- A.15.10 fprintf and sprint functions -- A.15.11 Timeseries function -- A.15.12 iddata function -- A.15.13 Size, length, and numel functions -- A.15.14 if, conditional functions and for, loop function -- A.16 Required ToolBoxes and dependencies -- Index -- Back cover.
• Description based on print version record.