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.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Preparation & stationarizing - p. 13 3. Chapter 3 Distribution evaluation and normalizing - p. 87 4. Chapter 4 Stochastic modeling - p. 133 5. Chapter 5 Goodness-of-fit & precision criteria - p. 187 6. Chapter 6 Forecasting time series by deep learning and hybrid methods - p. 265 7. Appendix MATLAB introduction and basic commands - p. 321 8. Index - p. 353
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.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Preparation & stationarizing - p. 13 3. Chapter 3 Distribution evaluation and normalizing - p. 87 4. Chapter 4 Stochastic modeling - p. 133 5. Chapter 5 Goodness-of-fit & precision criteria - p. 187 6. Chapter 6 Forecasting time series by deep learning and hybrid methods - p. 265 7. Appendix MATLAB introduction and basic commands - p. 321 8. Index - p. 353
