Stochastic Modeling

Stochastic Modeling

A Thorough Guide to Evaluate, Pre-Process, Model and Compare Time Series with MATLAB Software

1st Edition - April 13, 2022

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  • Authors: Hossein Bonakdari, Mohammad Zeynoddin
  • Paperback ISBN: 9780323917483
  • eBook ISBN: 9780323972758

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Description

Stochastic Modeling: A Thorough Guide to Evaluate, Pre-Process, Model and Compare Time Series with MATLAB Software allows for new avenues in time series analysis and predictive modeling which summarize more than ten years of experience in the application of stochastic models in environmental problems. The book introduces a variety of different topics in time series in the modeling and prediction of complex environmental systems. Most importantly, all codes are user-friendly and readers will be able to use them for their cases. Users who may not be familiar with MATLAB software can also refer to the appendix. This book also guides the reader step-by-step to learn developed codes for time series modeling, provides required toolboxes, explains concepts, and applies different tools for different types of environmental time series problems.

Key Features

  • Provides video tutorials on the use of codes
  • Includes a companion site with 3,000 lines of programming, 70 principal codes and 100 pseudo codes
  • Highlights multiple methods to Illustrate each problem

Readership

Environments, Soil science, Water engineering, hydrology, statistics

Table of Contents

  • Cover Image
  • Title Page
  • Copyright
  • Dedication
  • Table of Contents
  • Preface
  • Acknowledgments
  • Abbreviations
  • Chapter 1 Introduction
  • 1.1 Time series
  • 1.2 Stochastic and stochastic with exogenous variables
  • 1.3 Data preprocessing
  • References
  • Chapter 2 Preparation & stationarizing
  • 2.1 Missing data
  • 2.2 Detecting outliers
  • 2.3 Time series structure and attributes
  • 2.3.1 Trend in time series
  • 2.4 Stationarity
  • 2.5 Deterministic terms detection tests
  • 2.6 Stationarizing methods
  • 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
  • 3.7 Normalization transforms
  • 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
  • 4.6 Stochastic models equations
  • 4.7 Identify appropriate models and parameters' orders
  • 4.8 Estimation of stochastic models' parameters
  • 4.9 Univariate stochastic modeling
  • 4.10 Stochastic models with exogenous inputs
  • 4.11 Fitting stochastic and stochasticX models by econometric modeler app
  • 4.12 Invertibility constraint for MA models
  • 4.13 Chapter summary
  • 4.14 Exercise
  • References
  • Chapter 5 Goodness-of-fit & precision criteria
  • 5.1 Model adequacy
  • 5.2 Model parsimony
  • 5.3 Conventional performance measure
  • 5.4 Cross-validation in time series
  • 5.5 Exercise
  • References
  • Chapter 6 Forecasting time series by deep learning and hybrid methods
  • 6.1 Deep learning introduction
  • 6.2 Hybrid modeling
  • 6.3 Exercise
  • References
  • Appendix MATLAB introduction and basic commands
  • Index

Product details

  • No. of pages: 366
  • Language: English
  • Copyright: © Elsevier 2022
  • Published: April 13, 2022
  • Imprint: Elsevier
  • Paperback ISBN: 9780323917483
  • eBook ISBN: 9780323972758

About the Authors

Hossein Bonakdari

Hossein Bonakdari, Ph.D, P.Eng., earned his Ph.D in Civil Engineering at the University of Caen-France. He has worked for several organizations like most recently as faculty member of department of Soil and Agri-Food Engineering at Laval University, Quebec, Canada. His fields of specialization include: water resources management; hydrological modelling; artificial intelligence; sustainable development; time series. Results obtained from his researches have been published in more than 250 published papers in international journals (h-index=35). He has more than 150 presentations in national and international conference and has published two books. He has secured several external research funding sponsored by government and related water resources companies/industries. He is currently leading several research projects in collaboration with industrials partners. He is ranked among the top 2.5% of the world's top researchers on ResearchGate. In 2020, Dr Bonakdari classified in the list of 2% of the most influential people of science in the world.

Affiliations and Expertise

Department of Soils and Agri‐Food Engineering, Laval University, Quebec, Canada

Mohammad Zeynoddin

Mohammad Zeynoddin is currently Ph.D. candidate in the field of Soil and Environments at Department of Soils and Agri‐Food Engineering, Laval University, Québec, Canada. He holds Master of Water Engineering and Hydraulic Structure and Bachelor of Civil Engineering diploma. His research has primarily been focused on time series modeling to improve the accuracy of calculations of hydrological variables for monitoring, real time prediction, optimization, and automation of hydrological and environmental systems. Results of his research was 12 published papers in international journals with high Impact Factors. He received several awards and honors from universities during of his Master and PhD studies. He has a passion for art and sports. He holds several international sport certificates and championships.

Affiliations and Expertise

Ph.D. candidate in the field of Soil and Environments, Department of Soils and Agri‐Food Engineering, Laval University, Québec, Canada

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