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Cyclostationary Processes and Time Series

, astronomy, and biology. Cyclostationary Processes and Time Series: Theory, Applications, and Generalizations addresses these issues and;

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Generalizations of Cyclostationary Signal Processing

-cyclostationary processes and spectrally correlated processes; two classes of signals finding growing importance in areas such as mobile communications;

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Cyclostationarity Theory and Methods IV

, researchers and professionals with a timely guide on cyclostationary systems, nonstationary processes and relevant engineering applications.;

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Cyclostationarity

, researchers and professionals with a timely guide on cyclostationary systems, nonstationary processes and relevant engineering applications.;

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Periodically Correlated Random Sequences

Uniquely combining theory, application, and computing, this book explores the spectral approach to time series analysis The use;

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Time Series Analysis with Long Memory in View

, Time Series Analysis with Long Memory in View offers chapters that cover: Stationary Processes; Moving Averages and Linear Processes; Frequency;

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Introduction to Time Series and Forecasting

edition: A chapter devoted to Financial Time Series Introductions to Brownian motion, Levy processes and Ito calculus An expanded;

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Athens Conference on Applied Probability and Time Series Analysis

distribution, frequency domain methods, long range dependent processes, nonstationary processes, and nonlinear time series. The methods presented can;

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Time Series Analysis in Climatology and Related Sciences

This book gives the reader the basic knowledge of the theory of random processes necessary for applying to study climatic time series. It;

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Large Sample Inference For Long Memory Processes

Box and Jenkins (1970) made the idea of obtaining a stationary time series by differencing the given, possibly nonstationary, time series;

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Interrupted Time Series Analysis

regression analysis but limited background in the structure of time series processes and experiments.;

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Time Series Analysis of Long Memory versus Structural Breaks

time series analysis and then studies the long memory versus structural breaks debate. A detailed study of an error duration model gives a nice;

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U-Th Series Nuclides in Aquatic Systems

of U-Th series nuclides to "time" and "tag" in a variety of processes occurring in aquatic systems.State of art reviews on the;

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Long Memory in Economics

dependent p- cesses. Indeed, the realizations of non-homogeneous processes, e.g., switching regime and change-point processes, display the same;

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Statistical Inference for Financial Engineering

analyzing financial time series data. In order to describe the actual financial data, various stochastic processes, e.g. non-Gaussian linear;

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Introduction To Statistical Time Series

The subject of time series is of considerable interest, especially among researchers in econometrics, engineering, and the natural sciences;

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Forecasting Economic Time Series using Locally Stationary Processes

processes and compares this approach to classical forecasting methods by means of Monte Carlo simulations. An evaluation of the proposed procedure;

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Statistical Signal Processing of Complex-Valued Data

, nonstationary, and cyclostationary processes. Providing a systematic development from the origin of complex signals to their probabilistic description;

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Integrated Supply Chain Planning in Chemical Industry

simulation framework is designed taking into account time series models for modelling chemical production processes, linear optimization models for;

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Time Series and Panel Data Econometrics

of basic econometric and statistical techniques, and provides an account of stochastic processes, univariate and multivariate time series, tests;

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Stochastic Analysis of Scaling Time Series

Multi-scale systems, involving complex interacting processes that occur over a range of temporal and spatial scales, are present in a broad;

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Identification Of Time-Varying Processes

control and failure detection). The processes also assist signal processing in areas such as adaptive noise reduction, prediction of time series;

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Time Series Analysis

stochastic trends have been added. Three new chapters on long-memory discrete-time and continuous-time processes have also been created, whereas some;

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Empirical Likelihood and Quantile Methods for Time Series

., infinite variance processes, not only from the point of view of efficiency but also from that of robustness and optimality by minimizing prediction;

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Advanced Analysis and Learning on Temporal Data

dependency in clustering or classification processes. The last part of the book is dedicated to metric learning and time series comparison, it;

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Elements of Applied Stochastic Processes

introduction to stationary processes and time series analysis * Integrates a broad set of applications into the text * Utilizes a wealth of examples;

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Stochastic Processes and Calculus

specifically mathematical finance and time series econometrics. Over the past decades stochastic calculus and processes have gained great importance;

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