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Textbook:
Stochastic Modeling: A Thorough Guide to Evaluate, Pre-Process, Model and Compare Time Series with MATLAB SoftwareAuthor(s): Hossein Bonakdari
Description:
This course introduces a 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 coursebook 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 students 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 coursebook also guides students 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
The coursebook consists of six chapters. Chapter 1 includes the introduction and basic concepts,while the rest covers the steps in assessing,preprocessing,and modeling a time series and evaluation of the models for future predictions.At the end of each chapter, some exercises are provided for interested readers.
The FIRST chapter contains the introduction and a brief description of time series concept and methods of utilizing these forms of data in analyzing, modeling, and interpreting phenomena. The chapter also includes reasons for applying tests and preprocessing methods,as well as descriptions of stochastic model concepts.
The SECOND chapter focuses on the preparation and stationary concepts, tests, methodsofobtainingthestationarity,andhowtocodetheminMATLAB.Thecollected data may be incomplete and have missing data points or have components that make it unsuitable for interpretation and modeling. Therefore, in this section, methods of preparation of time series are presented.
The THIRD chapter describes the normal distribution and reasons for requiring normal data. Following the descriptions, the tests to evaluate normality of time series are presented.Finally,several transforms for normalizing of non-normal time series are provided and an instance is given for each one.
The FOURTH chapter is dedicated to stochastic modeling. In this chapter, the concepts of stochastic models, different types of this subcategory of statistical models, and properties of each are defined.After presenting theoretical descriptions and the pros and cons of each model,the chapter explains how to obtain the order of parameters for each model and the technique of modeling in MATLAB software.
The FIFTH chapter relates the methods of evaluation of the produced models.The surveying of the model’s functionality and precision is a prerequisite of each modeling procedure.In this section of the book,the tests and indices required to obtain the most accurate model with the simplest structure are presented. The criteria that assess the adequacy and parsimony of the model are introduced. Finally, the concepts of crossvalidation and how to evaluate the time series-based models by cross-validation are presented.
The SIXTH chapter is dedicated to deep learning modeling and integration of stochastic models with artificial intelligence models. The hybridization of the linear, nonlinear methods is a subject that has attracted scholars’attention widely.This method utilizes both linear and nonlinear method characteristics and therefore,most of the time produces better results than using individual models.Since the area of artificial models is vast, covering all of them is not possible, and is not within the scope of this book. Therefore,onlythemostrecentandsuccessfulLong-Short-TermMemorymodel,along with its hybridization with stochastic models,is introduced and described in this coursebook.
Stochastic Modeling_ A Thorough Guide to Evaluate, Pre-Process, Model and Compare Time Series with MATLAB Software (2022)
Stochastic Modeling_ A Thorough Guide to Evaluate, Pre-Process, Model and Compar.pdf
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