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An introduction to discrete-valued time series
A much-needed introduction to the field of discrete-valued time series, with a focus on count-data time series Time series analysis is an essential tool in a wide array of fields, including business, economics, computer science, epidemiology, finance, manufacturing and meteorology, to name just a few. Despite growing interest in discrete-valued time series—especially those arising from counting specific objects or events at specified times—most books on time series give short shrift to that increasingly important subject area. This book seeks to rectify that state of affairs by providing a much needed introduction to discrete-valued time series, with particular focus on count-data time series. The main focus of this book is on modeling. Throughout numerous examples are provided illustrating models currently used in discrete-valued time series applications. Statistical process control, including various control charts (such as cumulative sum control charts), and performance evaluation are treated at length. Classic approaches like ARMA models and the Box-Jenkins program are also featured with the basics of these approaches summarized in an Appendix. In addition, data examples, with all relevant R code, are available on a companion website. • Provides a balanced presentation of theory and practice, exploring both categorical and integer-valued series • Covers common models for time series of counts as well as for categorical time series, and works out their most important stochastic properties • Addresses statistical approaches for analyzing discrete-valued time series and illustrates their implementation with numerous data examples • Covers classical approaches such as ARMA models, Box-Jenkins program and how to generate functions • Includes dataset examples with all necessary R code provided on a companion website An Introduction to Discrete-Valued Time Series is a valuable working resource for researchers and practitioners in a broad range of fields, including statistics, data science, machine learning, and engineering. It will also be of interest to postgraduate students in statistics, mathematics and economics.
eBook
Digital Stabilization of a Switched Linear System with Commensurate Delays
An approach to the construction of a digital controller that stabilizes a continuous-time switched linear system with commensurate delays in control is proposed. The approach to stabilization sequentially includes the construction of a switched continuous-discrete closed system with a digital controller, the transition to its discrete model represented as a switched system with modes of various orders, and the construction of a discrete dynamic controller based on the quadratic stability condition for a closed switched discrete-time system.
Journal Article
Model Reduction for Discrete-Time Systems via Optimization over Grassmann Manifold
In this paper, we investigate h2-optimal model reduction methods for discrete-time linear time-invariant systems. Similar to the continuous-time case, we will formulate this problem as an optimization problem over a Grassmann manifold. We consider constructing reduced systems by both one-sided and two-sided projections. For one-sided projection, by utilizing the principle of the Grassmann manifold, we propose a gradient flow method and a sequentially quadratic approximation approach to solve the optimization problem. For two-sided projection, we apply the strategies of alternating direction iteration and sequentially quadratic approximation to the minimization problem and develop a numerically efficient method. One main advantage of these methods, based on the formulation of optimization over a Grassmann manifold, is that stability can be preserved in the reduced system. Several numerical examples are provided to illustrate the effectiveness of the methods proposed in this paper.
Journal Article
Discrete-event simulation and system dynamics for management decision making
In recent years, there has been a growing debate, particularly in the UK and Europe, over the merits of using discrete-event simulation (DES) and system dynamics (SD); there are now instances where both methodologies were employed on the same problem. This book details each method, comparing each in terms of both theory and their application to various problem situations. It also provides a seamless treatment of various topics--theory, philosophy, detailed mechanics, practical implementation--providing a systematic treatment of the methodologies of DES and SD, which previously have been treated separately.
eBook
Modeling and simulation of discrete-event systems
This book provides a comprehensive, systematic treatment of discrete event systems modeling and simulation, covering the five major breakthrough areas in modeling over the past sixty years, and integrating these areas into engineering s most widely used modeling and simulation systems.
eBook
Controlled Invariant Sets of Discrete-Time Linear Systems with Bounded Disturbances
This paper proposes two novel methods for computing the robustly controlled invariant set of linear discrete-time systems with additive bounded disturbances. In the proposed methods, the robustly controlled invariant set of discrete-time systems is obtained by solving the linear matrix inequality given by logarithmic norm and difference inequality. Illustrative examples are presented to demonstrate the obtained methods.
Journal Article
Design of Finite Time Reduced Order H∞ Controller for Linear Discrete Time Systems
The current article gives a new approach that is efficient for the design of a low-order H∞ controller over a finite time interval. The system under consideration is a linear discrete time system affected by norm bounded disturbances. The proposed method has the advantage that takes into account both robustness aspects and desired closed-loop characteristics, reducing the number of variables in Linear Matrix Inequalities (LMIs). Thus, reduced order H∞ controller parameters are given to guarantee a finite time H∞ bound (FTB-H∞) for a closed-loop system. The method of the finite time stability, that is proven in this paper by the Lyapunov theory, can be applied to a wide range of process models. Numerical examples demonstrating the effectiveness of the results developed are presented at the end of this paper.
Journal Article
The Time Series Classification of Discrete-Time Chaotic Systems Using Deep Learning Approaches
Discrete-time chaotic systems exhibit nonlinear and unpredictable dynamic behavior, making them very difficult to classify. They have dynamic properties such as the stability of equilibrium points, symmetric behaviors, and a transition to chaos. This study aims to classify the time series images of discrete-time chaotic systems by integrating deep learning methods and classification algorithms. The most important innovation of this study is the use of a unique dataset created using the time series of discrete-time chaotic systems. In this context, a large and unique dataset representing various dynamic behaviors was created for nine discrete-time chaotic systems using different initial conditions, control parameters, and iteration numbers. The dataset was based on existing chaotic system solutions in the literature, but the classification of the images representing the different dynamic structures of these systems was much more complex than ordinary image datasets due to their nonlinear and unpredictable nature. Although there are studies in the literature on the classification of continuous-time chaotic systems, no studies have been found on the classification of discrete-time chaotic systems. The obtained time series images were classified with deep learning models such as DenseNet121, VGG16, VGG19, InceptionV3, MobileNetV2, and Xception. In addition, these models were integrated with classification algorithms such as XGBOOST, k-NN, SVM, and RF, providing a methodological innovation. As the best result, a 95.76% accuracy rate was obtained with the DenseNet121 model and XGBOOST algorithm. This study takes the use of deep learning methods with the graphical representations of chaotic time series to an advanced level and provides a powerful tool for the classification of these systems. In this respect, classifying the dynamic structures of chaotic systems offers an important innovation in adapting deep learning models to complex datasets. The findings are thought to provide new perspectives for future research and further advance deep learning and chaotic system studies.
Journal Article