Download or read book Forecasting principles and practice written by Rob J Hyndman and published by OTexts. This book was released on 2018-05-08 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.

Download or read book Principles of Business Forecasting 2nd Ed written by Keith Ord and published by Wessex, Incorporated. This book was released on 2017-06 with total page 588 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition of Principles of Business Forecasting by Keith Ord, Robert Fildes, and newest author Nikolaos Kourentzes serves as both a textbook for students and as a reference book for experienced forecasters in a variety of fields. The authors' motivation for writing this book, is to give users the tools and insight to make the most effective forecasts drawing on the latest research ideas, without being overly technical. The book is unique in its design, providing an introduction to both standard and advanced forecasting methods, as well as a focus on general principles to guide and simplify forecasting practice for those with little or no professional experience. One of the book's key strengths is the emphasis on real data sets, which have been updated in this second edition. These data sets are taken from government and business sources and are used throughout in the chapter examples and exercises. Forecasting techniques are demonstrated using a variety of software platforms beyond just "R," and a companion website provides easy-to-use Excel(R) macros that users can access to conduct analyses. Another important innovation in the second edition is the tutorial support for using open-source R programs, making all the methods available for use both in courses and practice. After the introductory chapters, the focus shifts to using extrapolative methods (exponential smoothing and ARIMA), then to statistical model-building using multiple regression. The authors also cover more novel techniques including data mining and judgmental methods, which are gaining increasing attention in applications. The second edition also offers expanded material on data analytics, in particular neural nets together with software, and applications that include new research findings relevant and immediately applicable to operations, such as hierarchical modeling and temporal aggregation. Finally, the authors examine organizational issues of implementation and the development of a forecasting support system within an organization; relevant to every manager, or future manager, who must make plans or decisions based on forecasts. Please take a moment to review the companion website for additional content in the Appendices (Basic Statistical Concepts, overview of Forecasting Software, and Forecasting in R: Tutorial and Examples) the many data sets referenced in the chapters, macros such as the Exponential Smoothing and Trend Curve Marcos and Time Series Neural Network Analysis and student study materials.

Download or read book Principles of Forecasting written by J.S. Armstrong and published by Springer Science & Business Media. This book was released on 2001 with total page 880 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook summarises knowledge from experts and empirical studies. It provides guidelines that can be applied in fields such as economics, sociology, and psychology. Includes a comprehensive forecasting dictionary.

Download or read book Business Forecasting written by Michael Gilliland and published by John Wiley & Sons. This book was released on 2021-05-11 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Business Forecasting Second Edition written by A. Reza Hoshmand and published by Routledge. This book was released on 2009-12-04 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book emphasizes the rationale, application, and interpretation of the most commonly used forecasting techniques in business.

Download or read book Principles of Marketing Engineering 2nd Edition written by Gary L. Lilien and published by DecisionPro. This book was released on 2013 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 21st century business environment demands more analysis and rigor in marketing decision making. Increasingly, marketing decision making resembles design engineering-putting together concepts, data, analyses, and simulations to learn about the marketplace and to design effective marketing plans. While many view traditional marketing as art and some view it as science, the new marketing increasingly looks like engineering (that is, combining art and science to solve specific problems). Marketing Engineering is the systematic approach to harness data and knowledge to drive effective marketing decision making and implementation through a technology-enabled and model-supported decision process. (For more information on Excel-based models that support these concepts, visit DecisionPro.biz.) We have designed this book primarily for the business school student or marketing manager, who, with minimal background and technical training, must understand and employ the basic tools and models associated with Marketing Engineering. We offer an accessible overview of the most widely used marketing engineering concepts and tools and show how they drive the collection of the right data and information to perform the right analyses to make better marketing plans, better product designs, and better marketing decisions. What's New In the 2nd Edition While much has changed in the nearly five years since the first edition of Principles of Marketing Engineering was published, much has remained the same. Hence, we have not changed the basic structure or contents of the book. We have, however Updated the examples and references. Added new content on customer lifetime value and customer valuation methods. Added several new pricing models. Added new material on "reverse perceptual mapping" to describe some exciting enhancements to our Marketing Engineering for Excel software. Provided some new perspectives on the future of Marketing Engineering. Provided better alignment between the content of the text and both the software and cases available with Marketing Engineering for Excel 2.0.

Download or read book Principles of Business Forecasting 2nd Ed written by Keith Ord and published by Wessex, Incorporated. This book was released on 2017-08-15 with total page 588 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Bayesian Forecasting and Dynamic Models written by Mike West and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book we are concerned with Bayesian learning and forecast ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel opment has involved thorough investigation of mathematical and sta tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter.

Download or read book Sales Forecasting Management written by John T. Mentzer and published by SAGE. This book was released on 2004-11-23 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: Incorporating 25 years of sales forecasting management research with more than 400 companies, Sales Forecasting Management, Second Edition is the first text to truly integrate the theory and practice of sales forecasting management. This research includes the personal experiences of John T. Mentzer and Mark A. Moon in advising companies how to improve their sales forecasting management practices. Their program of research includes two major surveys of companies′ sales forecasting practices, a two-year, in-depth study of sales forecasting management practices of 20 major companies, and an ongoing study of how to apply the findings from the two-year study to conducting sales forecasting audits of additional companies. The book provides comprehensive coverage of the techniques and applications of sales forecasting analysis, combined with a managerial focus to give managers and users of the sales forecasting function a clear understanding of the forecasting needs of all business functions. New to This Edition: The author′s well-regarded Multicaster software system demo, previously available on cassette, has been updated and is now available for download from the authors′ Web site New insights on the critical area of qualitative forecasting are presented The results of additional surveys done since the publication of the first edition have been added The discussion of the four dimensions of forecasting management has been significantly enhanced Significant reorganization and updating has been done to strengthen and improve the material for the second edition. Sales Forecasting Management is an ideal text for graduate courses in sales forecasting management. Practitioners in marketing, sales, finance/accounting, production/purchasing, and logistics will also find this easy-to-understand volume essential.

Download or read book Fashion Forecasting written by Kathryn McKelvey and published by John Wiley & Sons. This book was released on 2009-03-16 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Everyone in the fashion business needs to develop an essential survival skill: how to interpret the intelligence provided by the fashion forecasting industry, to anticipate and respond to emerging trends. Lavishly illustrated in full colour throughout, with clear and relevant explanations of the processes involved, Fashion Forecasting is for fashion students and young professionals who already have acquired some fashion knowledge and skills. The authors interviewed the key players within this exciting industry and provide here fascinating insights into the dynamic contemporary fashion forecasting world and the varied creative roles within it - from intelligence gatherers to project consultants. The book covers how a trend is sourced, anticipated and developed, and also explores the interaction with marketing and brand development.

Download or read book Statistics for Business written by Derek Waller and published by Routledge. This book was released on 2010-05-14 with total page 523 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical analysis is essential to business decision-making and management, but the underlying theory of data collection, organization and analysis is one of the most challenging topics for business students and practitioners. This user-friendly text and CD-ROM package will help you to develop strong skills in presenting and interpreting statistical information in a business or management environment. Based entirely on using Microsoft Excel rather than more complicated applications, it includes a clear guide to using Excel with the key functions employed in the book, a glossary of terms and equations, plus a section specifically for those readers who feel rusty in basic maths. Each chapter has worked examples and explanations to illustrate the use of statistics in real life scenarios, with databases for the worked examples, cases and answers on the accompanying CD-ROM.

Download or read book Data Science for Supply Chain Forecasting written by Nicolas Vandeput and published by Walter de Gruyter GmbH & Co KG. This book was released on 2021-03-22 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method which includes experimentation, observation, and constant questioning must be applied to supply chains to achieve excellence in demand forecasting. This second edition adds more than 45 percent extra content with four new chapters including an introduction to neural networks and the forecast value added framework. Part I focuses on statistical "traditional" models, Part II, on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both forecast models and new concepts such as metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers. The book is replete with do-it-yourself sections with implementations provided in Python (and Excel for the statistical models) to show the readers how to apply these models themselves. This hands-on book, covering the entire range of forecasting—from the basics all the way to leading-edge models—will benefit supply chain practitioners, forecasters, and analysts looking to go the extra mile with demand forecasting.

Download or read book Demand Driven Forecasting written by Charles W. Chase and published by John Wiley & Sons. This book was released on 2009-07-23 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for Demand-Driven Forecasting A Structured Approach to Forecasting "There are authors of advanced forecasting books who take an academic approach to explaining forecast modeling that focuses on the construction of arcane algorithms and mathematical proof that are not very useful for forecasting practitioners. Then, there are other authors who take a general approach to explaining demand planning, but gloss over technical content required of modern forecasters. Neither of these approaches is well-suited for helping business forecasters critically identify the best demand data sources, effectively apply appropriate statistical forecasting methods, and properly design efficient demand planning processes. In Demand-Driven Forecasting, Chase fills this void in the literature and provides the reader with concise explanations for advanced statistical methods and credible business advice for improving ways to predict demand for products and services. Whether you are an experienced professional forecasting manager, or a novice forecast analyst, you will find this book a valuable resource for your professional development." —Daniel Kiely, Senior Manager, Epidemiology, Forecasting & Analytics, Celgene Corporation "Charlie Chase has given forecasters a clear, responsible approach for ending the timeless tug of war between the need for 'forecast rigor' and the call for greater inclusion of 'client judgment.' By advancing the use of 'domain knowledge' and hypothesis testing to enrich base-case forecasts, he has empowered professional forecasters to step up and impact their companies' business results favorably and profoundly, all the while enhancing the organizational stature of forecasters broadly." —Bob Woodard, Vice President, Global Consumer and Customer Insights, Campbell Soup Company

Download or read book Data Mining for Business Analytics written by Galit Shmueli and published by John Wiley & Sons. This book was released on 2019-10-14 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R

Download or read book Hands On Time Series Analysis with R written by Rami Krispin and published by Packt Publishing Ltd. This book was released on 2019-05-31 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build efficient forecasting models using traditional time series models and machine learning algorithms. Key FeaturesPerform time series analysis and forecasting using R packages such as Forecast and h2oDevelop models and find patterns to create visualizations using the TSstudio and plotly packagesMaster statistics and implement time-series methods using examples mentionedBook Description Time series analysis is the art of extracting meaningful insights from, and revealing patterns in, time series data using statistical and data visualization approaches. These insights and patterns can then be utilized to explore past events and forecast future values in the series. This book explores the basics of time series analysis with R and lays the foundations you need to build forecasting models. You will learn how to preprocess raw time series data and clean and manipulate data with packages such as stats, lubridate, xts, and zoo. You will analyze data and extract meaningful information from it using both descriptive statistics and rich data visualization tools in R such as the TSstudio, plotly, and ggplot2 packages. The later section of the book delves into traditional forecasting models such as time series linear regression, exponential smoothing (Holt, Holt-Winter, and more) and Auto-Regressive Integrated Moving Average (ARIMA) models with the stats and forecast packages. You'll also cover advanced time series regression models with machine learning algorithms such as Random Forest and Gradient Boosting Machine using the h2o package. By the end of this book, you will have the skills needed to explore your data, identify patterns, and build a forecasting model using various traditional and machine learning methods. What you will learnVisualize time series data and derive better insightsExplore auto-correlation and master statistical techniquesUse time series analysis tools from the stats, TSstudio, and forecast packagesExplore and identify seasonal and correlation patternsWork with different time series formats in RExplore time series models such as ARIMA, Holt-Winters, and moreEvaluate high-performance forecasting solutionsWho this book is for Hands-On Time Series Analysis with R is ideal for data analysts, data scientists, and all R developers who are looking to perform time series analysis to predict outcomes effectively. A basic knowledge of statistics is required; some knowledge in R is expected, but not mandatory.

Download or read book Forecasting Fundamentals written by Nada Sanders and published by Business Expert Press. This book was released on 2016-11-14 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is for everyone who wants to make better forecasts. It is not about mathematics and statistics. It is about following a well-established forecasting process to create and implement good forecasts. This is true whether you are forecasting global markets, sales of SKUs, competitive strategy, or market disruptions. Today, most forecasts are generated using software. However, no amount of technology and statistics can compensate for a poor forecasting process. Forecasting is not just about generating a number. Forecasters need to understand the problems they are trying to solve. They also need to follow a process that is justifiable to other parties and be implemented in practice. This is what the book is about. Accurate forecasts are essential for predicting demand, identifying new market opportunities, forecasting risks, disruptions, innovation, competition, market growth and trends. Companies can navigate this daunting landscape and improve their forecasts by following some well-established principles. This book is written to provide the fundamentals business leaders need in order to make good forecasts. These fundamentals hold true regardless of what is being forecast and what technology is being used. It provides the basic foundational principles all companies need to achieve competitive forecast accuracy.

Download or read book Principles of Business Forecasting written by J. K. Ord and published by South Western Educational Publishing. This book was released on 2013-01-01 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ord/Fildes PRINCIPLES OF BUSINESS FORECASTING, 1E, International Edition is designed for both students and current practitioners in a variety of fields. The authors’ motivation for writing this book is to give users the tools and insight to make the most effective forecasts. Ord/Fildes PRINCIPLES OF BUSINESS FORECASTING, 1E, International Edition is designed for users who have taken a first course in applied statistics or have an equivalent background and provides an introduction to both standard and advanced forecasting approaches. This new, first edition text presents general principles that are the basis of forecasting practice. The information and data used are from actual government and business sources wherever possible. Forecasting techniques are shown in a variety of software platforms and the chapter organization provides an overview of forecasting in a variety of situations using time series and cross-sectional data. The focus then shifts to using extrapolative methods in forecasting, followed by statistical model-building. Finally, the authors cover more advanced techniques in the latter chapters, including the selection of the best forecasting method based on available data and the construction of a forecasting system with an organization.