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| Author | : Melike Şah |
| Publisher | : |
| Total Pages | : 80 |
| Release | : 2005 |
| Genre | : Forecasting |
| ISBN | : |
| Author | : Cagdas Hakan Aladag |
| Publisher | : Bentham Science Publishers |
| Total Pages | : 143 |
| Release | : 2012 |
| Genre | : Mathematics |
| ISBN | : 1608053733 |
"Time series analysis is applicable in a variety of disciplines such as business administration, economics, public finances, engineering, statistics, econometrics, mathematics and actuarial sciences. Forecasting the future assists in critical organizationa"
| Author | : Witold Pedrycz |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 398 |
| Release | : 2012-11-29 |
| Genre | : Technology & Engineering |
| ISBN | : 3642334393 |
Temporal and spatiotemporal data form an inherent fabric of the society as we are faced with streams of data coming from numerous sensors, data feeds, recordings associated with numerous areas of application embracing physical and human-generated phenomena (environmental data, financial markets, Internet activities, etc.). A quest for a thorough analysis, interpretation, modeling and prediction of time series comes with an ongoing challenge for developing models that are both accurate and user-friendly (interpretable). The volume is aimed to exploit the conceptual and algorithmic framework of Computational Intelligence (CI) to form a cohesive and comprehensive environment for building models of time series. The contributions covered in the volume are fully reflective of the wealth of the CI technologies by bringing together ideas, algorithms, and numeric studies, which convincingly demonstrate their relevance, maturity and visible usefulness. It reflects upon the truly remarkable diversity of methodological and algorithmic approaches and case studies. This volume is aimed at a broad audience of researchers and practitioners engaged in various branches of operations research, management, social sciences, engineering, and economics. Owing to the nature of the material being covered and a way it has been arranged, it establishes a comprehensive and timely picture of the ongoing pursuits in the area and fosters further developments.
| Author | : Douglas C. Montgomery |
| Publisher | : John Wiley & Sons |
| Total Pages | : 670 |
| Release | : 2015-04-21 |
| Genre | : Mathematics |
| ISBN | : 1118745159 |
Praise for the First Edition "...[t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics." -MAA Reviews Thoroughly updated throughout, Introduction to Time Series Analysis and Forecasting, Second Edition presents the underlying theories of time series analysis that are needed to analyze time-oriented data and construct real-world short- to medium-term statistical forecasts. Authored by highly-experienced academics and professionals in engineering statistics, the Second Edition features discussions on both popular and modern time series methodologies as well as an introduction to Bayesian methods in forecasting. Introduction to Time Series Analysis and Forecasting, Second Edition also includes: Over 300 exercises from diverse disciplines including health care, environmental studies, engineering, and finance More than 50 programming algorithms using JMP®, SAS®, and R that illustrate the theory and practicality of forecasting techniques in the context of time-oriented data New material on frequency domain and spatial temporal data analysis Expanded coverage of the variogram and spectrum with applications as well as transfer and intervention model functions A supplementary website featuring PowerPoint® slides, data sets, and select solutions to the problems Introduction to Time Series Analysis and Forecasting, Second Edition is an ideal textbook upper-undergraduate and graduate-levels courses in forecasting and time series. The book is also an excellent reference for practitioners and researchers who need to model and analyze time series data to generate forecasts.
| Author | : Witold Pedrycz |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 399 |
| Release | : 2012-12-06 |
| Genre | : Mathematics |
| ISBN | : 1461313651 |
Fuzzy Modelling: Paradigms and Practice provides an up-to-date and authoritative compendium of fuzzy models, identification algorithms and applications. Chapters in this book have been written by the leading scholars and researchers in their respective subject areas. Several of these chapters include both theoretical material and applications. The editor of this volume has organized and edited the chapters into a coherent and uniform framework. The objective of this book is to provide researchers and practitioners involved in the development of models for complex systems with an understanding of fuzzy modelling, and an appreciation of what makes these models unique. The chapters are organized into three major parts covering relational models, fuzzy neural networks and rule-based models. The material on relational models includes theory along with a large number of implemented case studies, including some on speech recognition, prediction, and ecological systems. The part on fuzzy neural networks covers some fundamentals, such as neurocomputing, fuzzy neurocomputing, etc., identifies the nature of the relationship that exists between fuzzy systems and neural networks, and includes extensive coverage of their architectures. The last part addresses the main design principles governing the development of rule-based models. Fuzzy Modelling: Paradigms and Practice provides a wealth of specific fuzzy modelling paradigms, algorithms and tools used in systems modelling. Also included is a panoply of case studies from various computer, engineering and science disciplines. This should be a primary reference work for researchers and practitioners developing models of complex systems.
| Author | : Zhongjie Xie |
| Publisher | : World Scientific |
| Total Pages | : 298 |
| Release | : 1993 |
| Genre | : Science |
| ISBN | : 9789810210175 |
This book is a monograph on case studies using time series analysis, which includes the main research works applied to practical projects by the author in the past 15 years. The works cover different problems in broad fields, such as: engineering, labour protection, astronomy, physiology, endocrinology, oil development, etc. The first part of this book introduces some basic knowledge of time series analysis which is necessary for the reader to understand the methods and the theory used in the procedure for solving problems. The second part is the main part of this book ? case studies in different fields.
| Author | : Galina Ilieva |
| Publisher | : Infinite Study |
| Total Pages | : 13 |
| Release | : |
| Genre | : Mathematics |
| ISBN | : |
The goal of this paper is to propose a new method for fuzzy forecasting of time series with supervised learning and k-order fuzzy relationships. In the training phase based on k previous historical periods, a multidimensional matrix of fuzzy dependencies is constructed. During the test stage, the fitted fuzzy model is run for validating the observations and each output value is predicted by using a fuzzy input vector of k previous intervals. The proposed algorithm is verified by a benchmark dataset for fuzzy time series forecasting. The results obtained are similar or better than those of other fuzzy time series prediction methods. Comparative analysis shows the high potential of the new algorithm as an alternative to fuzzy prediction and reveals some opportunities for its further improvement.
| Author | : James C. Bezdek |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 267 |
| Release | : 2013-03-13 |
| Genre | : Mathematics |
| ISBN | : 147570450X |
The fuzzy set was conceived as a result of an attempt to come to grips with the problem of pattern recognition in the context of imprecisely defined categories. In such cases, the belonging of an object to a class is a matter of degree, as is the question of whether or not a group of objects form a cluster. A pioneering application of the theory of fuzzy sets to cluster analysis was made in 1969 by Ruspini. It was not until 1973, however, when the appearance of the work by Dunn and Bezdek on the Fuzzy ISODATA (or fuzzy c-means) algorithms became a landmark in the theory of cluster analysis, that the relevance of the theory of fuzzy sets to cluster analysis and pattern recognition became clearly established. Since then, the theory of fuzzy clustering has developed rapidly and fruitfully, with the author of the present monograph contributing a major share of what we know today. In their seminal work, Bezdek and Dunn have introduced the basic idea of determining the fuzzy clusters by minimizing an appropriately defined functional, and have derived iterative algorithms for computing the membership functions for the clusters in question. The important issue of convergence of such algorithms has become much better understood as a result of recent work which is described in the monograph.
| Author | : Ignacio Rojas |
| Publisher | : Springer |
| Total Pages | : 412 |
| Release | : 2017-07-31 |
| Genre | : Business & Economics |
| ISBN | : 3319557890 |
This volume of selected and peer-reviewed contributions on the latest developments in time series analysis and forecasting updates the reader on topics such as analysis of irregularly sampled time series, multi-scale analysis of univariate and multivariate time series, linear and non-linear time series models, advanced time series forecasting methods, applications in time series analysis and forecasting, advanced methods and online learning in time series and high-dimensional and complex/big data time series. The contributions were originally presented at the International Work-Conference on Time Series, ITISE 2016, held in Granada, Spain, June 27-29, 2016. The series of ITISE conferences provides a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing the disciplines of computer science, mathematics, statistics and econometrics.
| Author | : Haim Y. Bleikh |
| Publisher | : CRC Press |
| Total Pages | : 148 |
| Release | : 2016-02-24 |
| Genre | : Business & Economics |
| ISBN | : 1317010175 |
In Time Series Analysis and Adjustment the authors explain how the last four decades have brought dramatic changes in the way researchers analyze economic and financial data on behalf of economic and financial institutions and provide statistics to whomsoever requires them. Such analysis has long involved what is known as econometrics, but time series analysis is a different approach driven more by data than economic theory and focused on modelling. An understanding of time series and the application and understanding of related time series adjustment procedures is essential in areas such as risk management, business cycle analysis, and forecasting. Dealing with economic data involves grappling with things like varying numbers of working and trading days in different months and movable national holidays. Special attention has to be given to such things. However, the main problem in time series analysis is randomness. In real-life, data patterns are usually unclear, and the challenge is to uncover hidden patterns in the data and then to generate accurate forecasts. The case studies in this book demonstrate that time series adjustment methods can be efficaciously applied and utilized, for both analysis and forecasting, but they must be used in the context of reasoned statistical and economic judgment. The authors believe this is the first published study to really deal with this issue of context.
