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.

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