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

Product 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. Review Solves a wide variety of time series problems that bridge the gap between hydrology/climatology/remote sensing and data science/stochastic pipeline, and more From the Back Cover <p>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.</p> <p><i>Stochastic Modeling: A Thorough Guide to Evaluate, Pre-Process, Model and Compare Time Series with MATLAB Software</i> 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.</p> About the Author 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 w