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Neural Networks in Business Forecasting by G. Peter Zhang Pdf
Forecasting is one of the most important activities that form the basis for strategic, tactical, and operational decisions in all business organizations. Recently, neural networks have emerged as an important tool for business forecasting. Neural Networks in Business Forecasting provides researchers and practitioners with some recent advances in applying neural networks to business forecasting. A number of case studies demonstrating the innovative or successful applications of neural networks to many areas of business as well as methods to improve neural network forecasting performance are presented.
Business Forecasting by Michael Gilliland,Len Tashman,Udo Sglavo Pdf
Discover the role of machine learning and artificial intelligence in business forecasting from some of the brightest minds in the field In Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning accomplished authors Michael Gilliland, Len Tashman, and Udo Sglavo deliver relevant and timely insights from some of the most important and influential authors in the field of forecasting. You'll learn about the role played by machine learning and AI in the forecasting process and discover brand-new research, case studies, and thoughtful discussions covering an array of practical topics. The book offers multiple perspectives on issues like monitoring forecast performance, forecasting process, communication and accountability for forecasts, and the use of big data in forecasting. You will find: Discussions on deep learning in forecasting, including current trends and challenges Explorations of neural network-based forecasting strategies A treatment of the future of artificial intelligence in business forecasting Analyses of forecasting methods, including modeling, selection, and monitoring In addition to the Foreword by renowned researchers Spyros Makridakis and Fotios Petropoulos, the book also includes 16 "opinion/editorial" Afterwords by a diverse range of top academics, consultants, vendors, and industry practitioners, each providing their own unique vision of the issues, current state, and future direction of business forecasting. Perfect for financial controllers, chief financial officers, business analysts, forecast analysts, and demand planners, Business Forecasting will also earn a place in the libraries of other executives and managers who seek a one-stop resource to help them critically assess and improve their own organization's forecasting efforts.
Neural Networks in Business Forecasting by G. Peter Zhang Pdf
Forecasting is one of the most important activities that form the basis for strategic, tactical, and operational decisions in all business organizations. Recently, neural networks have emerged as an important tool for business forecasting. There are considerable interests and applications in forecasting using neural networks. This book provides for researchers and practitioners some recent advances in applying neural networks to business forecasting. A number of case studies demonstrating the innovative or successful applications of neural networks to many areas of business as well as methods to improve neural network forecasting performance are presented.
Neural Networks in Business: Techniques and Applications by Gupta, Jatinder N. D.,Smith, Kate A. Pdf
Neural Networks in Business: Techniques and Applications aims to be an introductory reference book for professionals, students and academics interested in applying neural networks to a variety of business applications. The book introduces the three most common neural network models and how they work, followed by a wide range of business applications and a series of case studies presented from contributing authors around the world.
Foreign-Exchange-Rate Forecasting with Artificial Neural Networks by Lean Yu,Shouyang Wang,Kin Keung Lai Pdf
This book focuses on forecasting foreign exchange rates via artificial neural networks (ANNs), creating and applying the highly useful computational techniques of Artificial Neural Networks (ANNs) to foreign-exchange rate forecasting. The result is an up-to-date review of the most recent research developments in forecasting foreign exchange rates coupled with a highly useful methodological approach to predicting rate changes in foreign currency exchanges.
Using Artificial Neural Networks for Timeseries Smoothing and Forecasting by Jaromír Vrbka Pdf
The aim of this publication is to identify and apply suitable methods for analysing and predicting the time series of gold prices, together with acquainting the reader with the history and characteristics of the methods and with the time series issues in general. Both statistical and econometric methods, and especially artificial intelligence methods, are used in the case studies. The publication presents both traditional and innovative methods on the theoretical level, always accompanied by a case study, i.e. their specific use in practice. Furthermore, a comprehensive comparative analysis of the individual methods is provided. The book is intended for readers from the ranks of academic staff, students of universities of economics, but also the scientists and practitioners dealing with the time series prediction. From the point of view of practical application, it could provide useful information for speculators and traders on financial markets, especially the commodity markets.
Recurrent Neural Networks for Short-Term Load Forecasting by Filippo Maria Bianchi,Enrico Maiorino,Michael C. Kampffmeyer,Antonello Rizzi,Robert Jenssen Pdf
The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementations of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. This work performs a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. The authors test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. The text also provides a general overview of the most important architectures and defines guidelines for configuring the recurrent networks to predict real-valued time series.
Theoretical and Applied Mathematics in International Business by Christiansen, Bryan,Shuwaikh, Fatima Pdf
In the past, practical applications motivated the development of mathematical theories, which then became the subject of study in pure mathematics where abstract concepts are studied for their own sake. The activity of applied mathematics is thus intimately connected with research in pure mathematics, which is also referred to as theoretical mathematics. Theoretical and Applied Mathematics in International Business is an essential research publication that explores the importance and implications of applied and theoretical mathematics within international business, including areas such as finance, general management, sales and marketing, and supply chain management. Highlighting topics such as data mining, global economics, and general management, this publication is ideal for scholars, specialists, managers, corporate professionals, researchers, and academicians.
Neural networks are state-of-the-art, trainable algorithms that emulate certain major aspects in the functioning of the human brain. This gives them a unique, self-training ability, the ability to formalize unclassified information and, most importantly, the ability to make forecasts based on the historical information they have at their disposal. Neural networks have been used increasingly in a variety of business applications, including forecasting and marketing research solutions. In some areas, such as fraud detection or risk assessment, they are the indisputable leaders. The major fields in which neural networks have found application are financial operations, enterprise planning, trading, business analytics and product maintenance. Neural networks can be applied gainfully by all kinds of traders, so if you're a trader and you haven't yet been introduced to neural networks, we'll take you through this method of technical analysis and show you how to apply it to your trading style. Neural networks have been touted as all-powerful tools in stock-market prediction. Companies such as MJ Futures claim amazing 199.2% returns over a 2-year period using their neural network prediction methods. They also claim great ease of use; as technical editor John Sweeney said in a 1995 issue of "Technical Analysis of Stocks and Commodities," "you can skip developing complex rules (and redeveloping them as their effectiveness fades) . . . just define the price series and indicators you want to use, and the neural network does the rest."
This book is designed to enable the reader to design and run a neural network-based project. It presents everything the reader will need to know to ensure the success of such a project. The book contains a free disk with C and C++ programs, which implement many of the techniques discussed in the book.
Foreign-Exchange-Rate Forecasting with Artificial Neural Networks by Lean Yu,Shouyang Wang,Kin Keung Lai Pdf
The book focuses on forecasting foreign exchange rates via artificial neural networks. It creates and applies the highly useful computational techniques of Artificial Neural Networks (ANNs) to foreign-exchange-rate forecasting. The result is an up-to-date review of the most recent research developments in forecasting foreign exchange rates coupled with a highly useful methodological approach to predicting rate changes in foreign currency exchanges. Foreign Exchange Rate Forecasting with Artificial Neural Networks is targeted at both the academic and practitioner audiences. Managers, analysts and technical practitioners in financial institutions across the world will have considerable interest in the book, and scholars and graduate students studying financial markets and business forecast will also have considerable interest in the book. The book discusses the most important advances in foreign-exchange-rate forecasting and then systematically develops a number of new, innovative, and creatively crafted neural network models that reduce the volatility and speculative risk in the forecasting of foreign exchange rates. The book discusses and illustrates three general types of ANN models. Each of these model types reflect the following innovative and effective characteristics: (1) The first model type is a three-layer, feed-forward neural network with instantaneous learning rates and adaptive momentum factors that produce learning algorithms (both online and offline algorithms) to predict foreign exchange rates. (2) The second model type is the three innovative hybrid learning algorithms that have been created by combining ANNs with exponential smoothing, generalized linear auto-regression, and genetic algorithms. Each of these three hybrid algorithms has been crafted to forecast various aspects synergetic performance. (3) The third model type is the three innovative ensemble learning algorithms that combining multiple neural networks into an ensemble output. Empirical results reveal that these creative models can produce better performance with high accuracy or high efficiency.
Artificial Higher Order Neural Networks for Economics and Business by Zhang, Ming Pdf
"This book is the first book to provide opportunities for millions working in economics, accounting, finance and other business areas education on HONNs, the ease of their usage, and directions on how to obtain more accurate application results. It provides significant, informative advancements in the subject and introduces the HONN group models and adaptive HONNs"--Provided by publisher.
Applied Time Series Analysis and Innovative Computing by Sio-Iong Ao Pdf
Applied Time Series Analysis and Innovative Computing contains the applied time series analysis and innovative computing paradigms, with frontier application studies for the time series problems based on the recent works at the Oxford University Computing Laboratory, University of Oxford, the University of Hong Kong, and the Chinese University of Hong Kong. The monograph was drafted when the author was a post-doctoral fellow in Harvard School of Engineering and Applied Sciences, Harvard University. It provides a systematic introduction to the use of innovative computing paradigms as an investigative tool for applications in time series analysis. Applied Time Series Analysis and Innovative Computing offers the state of art of tremendous advances in applied time series analysis and innovative computing paradigms and also serves as an excellent reference work for researchers and graduate students working on applied time series analysis and innovative computing paradigms.
Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications by Management Association, Information Resources Pdf
Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.
Artificial Neural Networks in Finance and Manufacturing by Kamruzzaman, Joarder,Begg, Rezaul,Sarker, Ruhul Pdf
"This book presents a variety of practical applications of neural networks in two important domains of economic activity: finance and manufacturing"--Provided by publisher.