SUSTAINABLE DEVELOPMENT
Innovate. Sustain. Transform.
Save up to 30% on top Physical Sciences & Engineering titles!
Stochastic Modeling
A Thorough Guide to Evaluate, Pre-Process, Model and Compare Time Series with MATLAB Software
- 1st Edition - April 13, 2022
- Authors: Hossein Bonakdari, Mohammad Zeynoddin
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 1 7 4 8 - 3
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 7 2 7 5 - 8
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 predictiv… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteStochastic 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.
- 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
Environments, Soil science, Water engineering, hydrology, statistics
- 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
- No. of pages: 366
- Language: English
- Edition: 1
- Published: April 13, 2022
- Imprint: Elsevier
- Paperback ISBN: 9780323917483
- eBook ISBN: 9780323972758
HB
Hossein Bonakdari
Prof. Hossein Bonakdari obtained his PhD in civil engineering from the University of Caen Normandy, France. He has worked for several organizations, most recently as Professor at the Department of Civil Engineering, University of Ottawa, Canada. He is one of the most influential scientists in the field of developing novel algorithms for solving practical problems through the decision-making abilities of artificial intelligence. His research also focuses on creating comprehensive methodologies in the areas of simulation modeling, optimization, and machine learning algorithms. The results obtained from his research have been published in international journals and presented at international conferences. He was included in the list of the world’s top 2% scientists, published by Stanford University, and is on the editorial board of several journals.
Affiliations and expertise
Associate Professor, Department of Civil Engineering, Faculty of Engineering, University of Ottawa, Ottawa, Ontario, CanadaMZ
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, CanadaRead Stochastic Modeling on ScienceDirect