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

Bonakdari; Zeynoddin

Softcover

ISBN 10: 0323917488 ISBN 13: 9780323917483

Publisher: Elsevier, 2022

Synopsis

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.

• 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

About this title

About the Authors

Dr. Hossein Bonakdari is a distinguished professor in the Department of Civil Engineering at the University of Ottawa, specializing in mathematical modeling and artificial intelligence (AI). A leading expert in AI-driven data analysis, he has pioneered advanced algorithms for real-time forecasting and big data interpretation, significantly improving the understanding and management of environmental systems.

Dr. Bonakdari has authored four books, published over 320 peer-reviewed journal articles, contributed to more than 20 book chapters, and delivered over 100 presentations at national and international conferences. As a respected editorial board member of several leading journals, he continues to shape research in his field. His groundbreaking contributions have earned him global recognition, ranking him among the top 2% of the world's scientists from 2019 to 2024.

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.

From the Back Cover

In recent years, a paradigm shift in automated assistance with real-time monitoring and the adjusting of appropriate forecasting models using data-driven techniques have been witnessed in several environmental fields. This shift in data analysis resulted in the generation of a significant amount of time series datasets massively produced from real-time monitoring of actual cases as well as from the outcomes of simulations of new optical, radar, sonar, and remote sensing measurement technologies. Typical environmental studies, decisions and strategies tied to time series data analysis involve understanding the nature of and modeling the time series, predicting its future values, and more importantly, understanding how it impacts and is impacted by other parameters. In many contexts, new theories and methods are needed to handle all these features for modeling and analysis. One of the commonly used smart data analysis methods is called stochastic methods which has been employed predominantly as a predictive model to resolve time-series data as efficient tools to extract patterns from complex data.

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’ experience in application of stochastic models in environmental problems. This book introduces a variety of different topics in time series in 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. Those 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, provide required toolboxes, understand the concepts, and apply different tools for any type of environmental time series problems.

Edition details

• Publisher: Elsevier
• Publication date: 2022
• Language: English
• ISBN 10: 0323917488
• ISBN 13: 9780323917483
• Binding: Paperback
• Number of pages: 370